DYNAMIC HW PRIORITY QUEUE BASED SCHEDULERS FOR EMBEDDED SYSTEM

International Journal of Embedded systems and Applications(IJESA) Vol.5, No.4, December 2015
DOI : 10.5121/ijesa.2015.5401 1
DYNAMIC HW PRIORITY QUEUE BASED
SCHEDULERS FOR EMBEDDED SYSTEM
Dinesh G Harkut1
and Dr. M. S. Ali2
1
Department of Computer Science & Engineering, Prof Ram Meghe College of
Engineering & Management, Badnera-Amravati. 444 701,M.S. (INDIA).
2
Principal, Prof Ram Meghe College of Engineering & Management, Badnera- Amravati.
444 701, M.S. (INDIA).
ABSTRACT
A real-time operating system (RTOs) is often used in embedded system, to structure the application code
and to ensure that the deadlines are met by reacting on events by executing the functions within precise
time. Most embedded systems are bound to real-time constraints with determinism and latency as a critical
metrics. RTOs are generally implemented in software, increases computational overheads, jitter and
memory footprint. Modern FPGA technology, enables the implementation of a full featured and flexible
hardware based RTOs, which helps in reducing to greater extent these overheads even if not remove
completely. Scheduling algorithms play an important role in the design of real-time systems. An Adaptive
Fuzzy Inference System (FIS) based scheduler framework proposed in this article is based on the study and
conclusion drawn from the research over the years in HW SW co-design domain. The proposed novel two
phase FIS based adaptive hardware task scheduler minimizes the processor time for scheduling activity
which uses fuzzy logic to model the uncertainty at first stage along with adaptive framework that uses
feedback which allows processors share of task running on multiprocessor to be controlled dynamically at
runtime. This Fuzzy logic based adaptive hardware scheduler breakthroughs the limit of the number of
total task and thus improves efficiency of the entire real-time system. The increased computation overheads
resulted from proposed two phase FIS scheduler can be compensated by utilising the basic characteristics
of parallelism of the hardware as scheduler being migrated to FPGA.
KEYWORDS
Hardware scheduler, Job priority, Real-time operating system, Reconfigurable computing, Scheduling
Algorithms, ANFIS, HW/SW co design.
1.INTRODUCTION
Technology innovations drive the today’s consumer market. Many technologies that were not
available a few years ago are quickly being adopted into common use. The technological
advances of microelectronics have radically changed, among many others, the scenario of modern
embedded real-time systems. Embedded devices are special purpose processor often designed to
serve their unique purpose. These processor finds application in almost variety of products within
different technical areas such as industrial automation, consumer electronics, automotive industry
and communications and multimedia systems. Product ranging from train and airplanes to
microwave ovens and washing machines are controlled by embedded systems. Sharp decrease in
semiconductor prices and coupled with increase in performance, catalyst the rapid increase in the
complexity of embedded applications. Furthermore, the high level of integration of silicon
technology opens up many interesting possibilities also for the world of re-programmable

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hardware platforms. In embedded systems a real-time operating system (RTOS) is often used in
order to structure the application code and ensure that deadlines are met. Moreover, RTOS also
reduces the development time of complex embedded applications through hardware abstraction
and multitasking. Further, intensified market pressure to rapidly develop cheaper product can also
be address by using RTOS in embedded system product development but at the cost of several
forms of overheads.
One of the constant challenges for real-time system designers is building a platform that can meet
the timeliness requirements of the system where mere logical accuracy of its computation will not
serve its purpose [1]. Thus the primary design goal of the RTOses is to minimizing the overheads,
reduce latency and maximizing the determinism This paper is organized as follows. Section 2 is
an overview of the Hardware/Software co design approaches. Section 3 describes related work of
other research projects, proposed model is discussed in section 4 and section 5 is summary and
conclusion from mainly previous work and related work.
2. HARDWARE SOFTWARE CO-DESIGN ARCHITECTURE
In embedded systems, RTOs is often used in order to structure the application code and ensure
that deadlines are met. The notions of best-effort and real-time processing have fractured into a
spectrum of processing classes with different timeliness requirements including desktop
multimedia, soft real-time, firm real-time, adaptive soft real-time, rate-based and traditional hard
real-time [2-3]. Real Time systems may be Hard-real time where missing deadline is catastrophic
or Soft-real time where, occasional violation of deadline may not result in useless execution of the
application but decreases utilization [4].
Traditionally RTOS’s are implemented in software, but major drawbacks of standard software
based RTOS’s is that they suffer from computational overheads, jitter and often a large memory
footprint. RTOS computational overheads is caused mainly by tick interrupt management, which
get even worse with more task and high tick frequencies, but also task scheduling , resource
allocation and de-allocation, deadlock detection and various other OS/API functions take
execution time from the task running on the CPU.
The overhead due to the software kernel contributes to the degradation the overall performance of
RTOS based embedded systems at the cost of high flexibility. On the other hand, task
implemented as hardware modules placed in Hardware have the characteristics of high
performance along with low flexibility and high cost.
The trend of utilizing reconfigurable FPGA’s, which can be programmed absolutely infinite
number of times, is drastically increasing. FPGA supports for high fabrication density, parallel
computing and low cost as compared to ASIC made it possible to implement established software
algorithms i.e. real-time kernel activity like scheduling, inter-process communications, interrupt
management, resource management, synchronization and time management controls in hardware
and thus consequently decreases system overhead, improve predictability and increases response
time.
As a tradeoffs reconfigurable and hardware/software co-design approaches that offer real time
capabilities and enhanced predictability of hardware and flexibility of software to support
increasing complex systems become more feasible solution. (Figure 1).

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Figure 1 – Hw/Sw System Architecture
Recent advancements in FPGA technology have made it more economically feasible to explore
migrating functionality across the hardware/software boundary. The flexibility of the FPGA
fabric and availability of configurable soft IP components has opened the potential to rapidly and
economically investigate different hardware/software partitions. This approach reach a level of
maturity that are allowing system designers to perform hardware/software co-design of operating
system core functionality such as time management and task scheduling that allow the advantages
of higher level program development while achieving the performance potential offered by
executions of these functions in parallel hardware circuits.
3. LITERATURE REVIEW
The main source of indeterminism in real time systems are speculative components like varying
instruction cycle time caused by pipeline, branch predictions, varying execution time of RTOs
kernel functions, external asynchronous interrupts etc. Migrating major real time kernel
functionality from software to hardware, it is possible to remove jitter, lessen CPU overhead and
improve the predictability of the system. Over the period of time, various attempts have been
made to design the models and systems to overcome these problem and some of them were
discussed in remaining section.
Lennart Lindh et al. [5-6] proposed a system FASTCHART; consist of hardware based RT kernel
capable of handling 64 task with 8 different priorities. FASTCHART is a RISC based
uniprocessor system which uses ID’s of tasks with various queues to make efficient
implementation feasible.
POLIS – A model proposed by F. Balarin, G. Berry, F. Boussinot et al. [7-8], is a Co-Design
Finite State Machine (CSFM) synthesis model, supports globally asynchronous and locally
synchronous computation. The POLIS generates C-code for processor and the optimized
hardware. Entire implementation is splits between Software and ASICs. This model does not
support large and complex design as large and complexity of processors makes the static
estimation difficult. However, Co-simulation is provided using Ptolemy environment [9].
FASTHARD - a hardware based general purpose processors, proposed by Lindh et al. [10], is
extension to his earlier work FASTCHART. Like FASTCHART, system is limited in supports for
customization and scalability but supports features like rendezvous, external interrupts, periodic
start and termination of task without CPU interference.
Processor
S/W API
I S R
Hardware
(FPGA/ASIC)
Resource
Manager
Task
Manager
IRQ
Manager

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The COSYMA system is HW/SW co-design fine or coarse grained partitioning model design
proposed by R. Ernst et al. [11-12], aims speedup software executions to meet timing constraints.
It uses simulated annealing for partitioning along with profiling and symbolic approaches are
used to calculate timing information. Though it does not support burst-mode communication, uses
list and path based are used to estimate execution time of hardware.
RTU (Real Time Unit), a multi-processor system proposed by J. Adomat et al. [13] uses single
interrupt input of each CPU to control and context switching. Lindh et al. [14] also proposes
extensible multiprocessor system - SARA, which can be used together with RTU to remove the
all scheduling and tick processing overheads.
T. Samuelsson et al. [15] benchmark the RTU, SARA on Rhealstone. The result seems to be
more deterministic. J. Lee et al. [16] further integrate the RTU with δ-framework for co-design.
In [17] S. Nordstrom et al. adapt µC/OS II RTOS with uniprocessor to boost the performance. S.
Nordstrom et al. [18] configurability is added and commercialize by Prevas AB. RTU suffers
from some of the limitations as it lacs support for counting semaphore, mutex, deadline
detection/preventions & dynamic priorities which limits its practical usefulness.
STRON system, which basically based on and extension to µTRON project is proposed by T.
Nakano et al. [19]. In this system basic system calls and functionality has been implemented in
hardware kernel which results in increasing speedup and reducing jitter. Some of the features not
supported by hardware are implemented in a small micro kernel. Unbounded priority inversion
and lack of support for high granularity tick frequency are the basic limitation of STRON.
In [20-21], R. Gupta et al. developed VULCAN - Hardware/Software partitioning tool, which
runs in polynomial time, uses heuristic graph partitioning algorithm. The ultimate goal is to
minimize hardware cost while maintaining timing constraints. The original description was in
Hardware-C [22], which is mapped to fine grained Control-Data Flow Graph. Test results are
missing in the paper.
P. Chou et al. [23-24], proposes automated interface synthesis hardware software co-design
framework for embedded system- CHINOOK. It ensures timing constraints [25] and also supports
mapping of an embedded system model to one (or more) processor and peripherals.
H. De. Man et al. proposed heterogeneous hardware/software DSP system COWARE in [26],
which is originally based on [27] and is basis of commercial CoWare N2C. COWARE system
allows co-specification using existing languages VHDL, DFL, Sliage & C but imposes increased
demands on generation of exhaustive library elements. This model supports the re-use and
encapsulation of hardware and software by a clear separation between functional and
communication behaviour of a system components.
In [28] Bjorn B. Brandenburg et al. discuss the LITMUSRT
project is a soft real-time extension of
the Linux kernel with focus on multiprocessor real-time scheduling and synchronization. The
primary focus is to provide a useful experimental platform for applied real-time systems research.
It supports the sporadic task model with both partitioned and global scheduling [29]. LITMUSRT
is not yet stable interfaces.
A.Parisoto et al. [30] suggested F-Timer framework which is FPGA based task scheduler targeted
at general purpose processor, capable of managing 32 tasks with 64 different priorities. System
does not have any hardware support for task synchronization and resource handling. Paper also
does not discussed about scheduling algorithm employed.

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J. Stankovic et al. [31] takes a radically different approach task scheduling than normal RTOS’s.
Spring kernel is basically designed for large and complex multiprocessor based RTOS. Task
management is based on dynamic and speculative planning based scheduling implement through
heuristic algorithm and tree search which makes it capable to handle fine granularity of task
deadlines. However large amount of pre-calculation overheads become the major bottleneck for
overall speedup of the system.
J. Hildebrandt et al. describes hardware implementation of dynamic scheduling coprocessor in
[32]. It is basically hardware scheduling accelerator which can be configured for several different
algorithms along with the most advanced Enhanced Least Laxity First (ELLF). This system has
increases the overall determinism but at the cost of higher complex logic and could no address
trashing of task.
In [33-34], I. Mooney et al. presents hardware/software co-design RTOs framework, δ-
Framework which supports 30 different processors. This framework generates all HDL code
which can be implemented in FPGA. The system is cost effective as far as overall speedup and
hardware area (number of gates) is concerned. More work on SOC was conducted [35] to
integrate priority inheritance and deadlock avoidance mechanism.
V.Mooney et al. [36], design and developed configurable hardware scheduler to improve
response time, interrupt latencies and CPU utilization and supports high tick frequency. The
interrupt controller in scheduler supports 8 external interrupts each can be configured for
dispatching a specific task and supports three different algorithms which can be change at run
time.
Issues of extension to OS and flexibility arises out of moving entire OS to hardware can be
overcome in model propose by Z. M. Wirthlin et al. in [37]. The nano-processor provides
upgradability, flexibility and also enhancing the execution time by moving selected inefficient OS
services in hardware to save on power consumption to a great extent as shown in several studies.
Leveraging the potential of hardware parallelism, Paul Kohot et al. in [38], developed Real-Time
Manager (RTM). Routine housekeeping tasks are implemented in hardware and thus relieve the
processor for critical functions which boosts the overall performance. RTM supports static
priority scheduling and handles task, time and event management. The author claims RTM
decreases RTOS overheads by 90% decreases response latency by 81%.
In [39] M.Vetromille et al. describes how low tick granularity can cause jitter and result in
deadline misses. HaRTS supports high tick frequency and thus reduce jitter without lower CPU
available time for task to process. It requires less chip area and uses less power than additional
processor but more complex to implement.
Communication speed between RTOS and hardware overshadowed the speed gain by hardware
scheduler. This problem has been overcome by intelligent design proposed by S. Chandra et al. in
[40]. The Hardware RTOS implemented for accelerating eCos, HW-eCos is interfaced to an
ARM processor requires less gates to implement and provides better speedup. Paper does not
discuss the number of tasks and resources supported by this system.
Z. Murtaza, S. Khan et al. describe SRTOS in [41] is aim at real-time DSP application targeted on
AVZ21 DSP processor. However paper doesn’t provide any experimental test result but system
supports additional instruction for fast resource allocation and context switching.

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H-Kernel system describe by M. Song et al. [42], is an outcome of through use of FPGA and
thoughtful HW/SW co-design for specific application. Nevertheless H-Kernel is suitable for
system with small numbers of task and increases performance in the tune of 50-60%, system
become more complex and bulky as number of task increases.
ARPA-MT system proposed by A. S. R. Oliveira et al. in [43] presents multi threading processor
with five stage pipeline. It supports heterogeneous task and context switches without hampering
the processor performance.
Luis Almeida et al. in [44-45] describe the details about OReK_CoP, Hardware implementation
of OReK Real-Time Kernel. All kernel functions execute in absolute time and almost in parallel,
without interfering CPU – improves determinism and improve resource utilization. Better and
more direct connections between CPU and coprocessor would have removed quiet large latency
introduced due to PLB bus interface in this system.
Many HW SW based solutions have been proposed to reduce the context switching time [46].
Xaingrong Zhou, Peter Petrov et al. presented model by converging compiler, micro-architecture
and OS kernel to reduce the context switching cost and improve overall responsiveness. In this
system context switching may be deferred until next switch point to limit the number of context
registers required to hold state. This arrangement may result in more deadline miss which can be
avoided by more complex and good RTOS kernel design.
N. Maruyama et al. in [47] proposed architecture ARTESSO, where RTOS, checksum
calculation, memory copying and TCP header rearrangement are ported to hardware. It uses novel
virtual queue instead of FIFO based queues used in RTU and STRON, which are logic expensive.
The author claims that this system is 6-9 times faster than STRON and 7 times more energy
efficient than its software counterpart.
Numbers of research projects have been targeted to approach the task of designing OS for FPGA
based reconfigurable computers (RC). Hayden Kwok-Hay et al. [48-49] describe BORPH, an
operating system designed for FPGA-based RC which provides native kernel support for FPGA
hardware and offers a homogeneous UNIX interface for both software and hardware processes.
All the services from the software kernels are inherited in hardware processes. Performance of
real-time wireless digital signal processing system based on BORPH will be presented.
HartOS proposed by Lange A.B. et al. is Hardware implemented Real-Time Operating System) in
[50] is designed to be very flexible and support most of the features normally found in a standard
software RTOS directly in hardware without sacrificing flexibility. HartOS has been compared to
the commercial Sierra kernel, its performance outperforms that of the Sierra Kernel [51]. The
HartOS the ability to let the kernel run at a higher clock frequency than the microprocessor,
which allows more tasks to be processed serially at the same tick frequency, and speed up the part
of the API functions executed in the kernel.
Scheduling algorithm plays as important role in the design of real-time systems which involves
allocation of resources and time to jobs in such way that certain performance requirements are
met. Most of the model discussed and reviewed are mainly focused on to improve the
performance by migrating some of the house keeping routine jobs from software to hardware with
a aim to leverage the potential of parallel processing of hardware which can further be improved
to a greater extent if more realistic scheduling algorithm is devise and migrate it on hardware to
assist processor and RTOs so as to increase the overall performance without increasing memory
footprint and power consumptions.

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Comparative study of various methodologies/models reviewed in the literature is given in the
Table1[52].
4. PROPOSED ANFIS BASED TASK SCHEDULER
We are proposing ANFIS based hardware scheduler framework which is discussed in subsequent
paragraph basically consist: Global Fuzzy scheduler – Long term scheduler and Local Adaptive
scheduler – Short term scheduler.
Both of these scheduler work in cascade and are migrated on hardware which will work in
synchronous with processor and RTOs to fulfil the overall systems objectives as illustrated in
figure 2.
Figure 2 Proposed ANFIS Task Scheduler
In this framework, task queue is maintained in FCFS basis which is feed to Fuzzy Task scheduler
which acts as long term global scheduler. To build a fuzzy system, inputs and output(s) to it must
be first selected and partitioned into appropriate conceptual categories which actually represent a
fuzzy set on a given input or output domain. Job priority, deadline and CPU time are selected as
input to the Fuzzy Inference System (FIS) [53-54], which consist of five stages:
1. Fuzzification of inputs variables
2. Applying fuzzy operators
3. Applying implication methods
4. Aggregating outputs variables
5. Defuzzification of output variables
Here Madani’s Fuzzy inference method or TSK or simply Sugeno method of fuzzy inference may
be used [55]. Output is single value which is treated as Job Processing Priority (JPP) and
maintained in job ready queue as per the newly calculated JPP which in turn feed to Adaptive
Task Scheduler a second stage scheduler.
Task Arrivals
Task
Queue
Adaptive Task
Scheduler
Task
dispatcher’s
Queue
Short Term
Local Scheduler
Fuzzy Task
Scheduler
Ready
Queue
Long Term
Global Scheduler
Resources
Synchronizer
Blocked/Resource
Waiting
Queue
.
P#1
P#2
P#n
Multi Processors
Environment
Hardware Task Scheduler

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Table 1 - Comparative study of various methodologies/models
The accuracy of schedulability analysis of system is largely depends on the accuracy with witch
the worst-case execution time (WCET) have been derived which is nothing but the maximum
amount of time each job can take to execute. The disadvantage of using WCETs under traditional
task model like periodic, sporadic etc., is that system may be deemed un-schedulable even if they
would function correctly most of the time when deployed. This drawback can be overcome by
making our scheduler adaptive to the runtime varying conditions, to allocate per-task processors
time share, instead of always using constant share allocation based on constant WCET and
readjusting the priority of task. Overall quality-of-service (QoS) can be improved by ignoring the

9
transient overload conditions. Dispatcher will dispatch the task from task dispatcher queue to
processors bank to get serve. Further resource synchronisation is used to optimise the block on
resource queue. Processors share allocations are adjusted using feedback and resource
synchronisation techniques [56].
This proposed fuzzy-adaptive feedback based scheduling algorithm based framework in capable
of handling multiprocessor environment. As this entire scheduling task is proposed to be port on
hardware (FPGA), increased computation requirement can be compensated by parallelism of
hardware with additional advantage of increase in determinism and lessening burdening of the
processor, which results in increase in overall performance of the system.
5. CONCLUSION
The conclusion from a comprehensive literature review of the publication throughout the last
three decades, is that the major drawback from software based RTO’s can be removed by
implementing the entire/ partial kernel of a real-time operating system in hardware. High
fabrication density, parallel computing capability, flexibility and low developmental cost results
in drastic increase in utilization of reconfigurable FPGA for implementation of a full featured and
flexible hardware based RTOs.
Implementing a real-time kernel in hardware makes it possible to draw benefits from hardware
characteristics such as parallelism and determinism. The execution time of real-time functions
gets deterministic and task switch can be performed without any CPU time delay [6,30]. When
real-time kernels are implemented in software, one of the disadvantages is that the execution time
for the service calls will have a minimum and a maximum time. The time gap can be big and the
worst-case time is one of the factors that will decide the utilization factor of the system.
Moreover, speculative component along with varying number of tasks, complex scheduling
algorithm further decrease the determinism. In hardware, the time gap can be designed to be close
to 0, which leads to predictable time behaviour, simpler timing analysis of the system and almost
no overhead. It is also easier to debug tasks since different protection modes are not required [57-
60].
A hardware kernel executes in parallel to the CPU, which relieves pressure from the CPU which
gets almost 100% execution time for the application tasks. There is less software code in memory
since the functionality is implemented in hardware instead [30].
A software OS will generate a clock tick interrupt to the CPU when either it is executed or the
lists of tasks (queues) are worked at or new periodic delay times are calculated for the tasks. With
the hardware kernel in the system, it checks all queues concurrently and only generates an
interrupt to the CPU when there is to be a task switch [57,61]. Another advantage of having the
kernel in hardware is the possibility to use complex scheduling algorithms, unlimited of different
queue types without any performance loss. Also there is an improved understandability and
complexity reduction when the system is divided into parts [57-58]. ANFIS based hardware task
scheduler will definitely results in increasing the overall performance of the system. The
simulation results showed that the implementation of task management by hardware kept the
correctness of system call, at the same time reduced the execution time of system calls and the
overhead of processor. Implementation details and result analysis is beyond the scope of this
paper.

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REFERENCES
[1] D.Stewart, “Introduction to Real Time”, Embedded systems programming, CMP Media, November
2001.
[2] Z.Deng, J.W. Liu and S. Sun, “Dynamic scheduling of hard real-time application in open system
environment”, Tech. Rep., University of Illinois at Urbana-Champaign 1996.
[3] S.M.Petters, “Bounding the execution time of real-time task on modern processors”, in Proceeding of
7th International Conference Real-Time Computing Systems and Applications, Cheju Island, pp.
498-502, 2000.
[4] J.Zhu, T.G. Lewis, W. Jackson and R.L. Wilson, “Scheduling in hard real-time applications”, IEEE
software, Volume 12, pp. 54-63, 1995.
[5] L.Lindh, F. Stanischewski, “FASTCHART - Performance, Benefits and Disadvantages of the
Architecture”, in Proceeding of 5th Euromicro Workshop on Real-Time Systems, 1993.
[6] L.Lindh, and F. Stanischewski, “FASTCHART- Idea and Implementation”, in IEEE International
Conference on Computer Design (ICCD), Boston, USA, Oct.1991.
[7] F.Balarin, M. Chiodo, P. Giusto, H. Hsieh, A. Jurecska, L. Lavagno, C. Passerone, A. Sangiovanni-
Vincentelli, E. Sentovich, K. Suzuki and B. Tabbara. “Hardware-Software Co-Design of Embedded
Systems: The POLIS Approach”, Kluwer Academic Publishers, 1997.
[8] G.Berry and G. Gonthier. “The Esterel Synchronous Programming Language: Design, Semantics,
Implementations”, Journal Science of Computer Programming archive, Elsevier North-Holland, Inc.
Amsterdam, The Netherlands, Volume 19 Issue 2, pp. 87-152, Nov. 1992.
[9] J.T.Buck, S. Ha, E.A. Lee and D.G. Messerschmitt. “Ptolemy: A Framework for Simulating and
Prototyping Heterogeneous Systems”, International Journal of Computer Simulation, special issue on
“Simulation Software Development”, pp.155-182, April 1994.
[10] L.Lindh, “FASTHARD - a fast time deterministic hardware based real-time kernel”, in Proceedings of
Real-Time Systems, 4th Euromicro workshop, pp. 21-25, June 1992.
[11] R Ernst, D Herrman, J. Henkel, Th. Benner, W. Ye, U. Holtmann and M. Trawny. “The COSYMA
environment for hardware software co-synthesis of small embedded systems”, IEEE Micro, pp.159-
166, 1996.
[12] R.Ernst, D. Herman, A. Osterling, T. Benner, T. Scholz and W. Ye. “The COSYMA system”, in
Hardware Software Co-Design: Principles and Practice , Kluwer Academic Publishers,1997.
[13] J.Adomat, J. Furunas, L. Lindh, and J. Starner, “Real-time kernel in hardware RTU: a step towards
deterministic and high-performance real-time systems”, in Proceedings of the 8th Euromicro
Workshop on Real-Time Systems, L'Aquila, pp. 164-168, Jun. 1996.
[14] L.Lindh, T. Klevin, L. L. T. Klevin, and J. Furunäs, “Scalable architecture for real-time applications
sara”, in CAD & CG’99, pp. 208-211, 1999.
[15] T.Samuelsson, M. Åkerholm, P. Nygren, J. Starner, and L. Lindh, “A comparison of multiprocessor
real-time operating systems implemented in hardware and software”, in International Workshop on
Systems Implemented in Hardware and Software, Advanced Real-Time Operating System Services
(ARTOSS), 2003.
[16] J.Lee, I. Mooney, V.J., A. Daleby, K. Ingstrom, T. Klevin, and L. Lindh, “A comparison of the RTU
hardware RTOS with a hardware/software RTOS”, in Proceeding ASP-DAC '03 Design Automation
Conference Asia and South Pacific, ACM New York, USA, pp.683-688, Jan. 2003.
[17] S.Nordstrom, L. Lindh, L. Johansson, and T. Skoglund, “Application specific real-time microkernel in
hardware”, in proceedings of 14th IEEE-NPSS Real Time Conference, pp. 4-9, Jun. 2005.
[18] S.Nordstrom and L. Asplund, “Configurable hardware/software support for single processor real-time
kernels”, pp. 1-4, Nov. 2007.
[19] T.Nakano, A. Utama, M. Itabashi, A. Shiomi, and M. Imai, “Hardware implementation of a real-time
operating system”, in proceeding of IEEE International Symposium of 12th TRON project, Tokoy,
Japan, pp. 34-42, Nov. 1995.
[20] R.Gupta. “Co-Synthesis of Hardware and Software for Digital Embedded Systems”, the Springer
International Series in Engineering and Computer Science, Volume 329, 1995.
[21] G.DeMicheli, R. Gupta, D. C. Ku, F. Mailhot and T. Truong. “The Olympus Synthesis System for
Digital Design”, Design & Test of Computers, IEEE Volume 7, Issue 5, pp. 37-53, Oct. 1990.
[22] D.C.Ku and G. DeMicheli. “HardwareC – a language for hardware design Ver 2.0” CSL Technical
Report CSL-TR-90-419, Stanford, April 1990.

11
[23] P.Chou, Ross Ortega and Gaetano Borriello. “The Chinook Hardware Software Co-Synthesis
System”, in Proceedings of the International Symphosium on System Synthesis, pp. 22-27, Sept.
1995.
[24] P.Chou, E. Walkup and G. Borriello. “Scheduling for Reactive Real-Time Systems”. IEEE Micro
archive Journal, IEEE Computer Society Press Los Alamitos, CA, USA. Volume 14, Issue 4, pp. 37-
47, August 1994.
[25] D.Ku and G.De Micheli. “High-level Synthesis of ASICs under Timing and Synchronization
Constraints”. Kluwer Academic Publishers, Norwell, MA, USA , ISBN:0-7923-9244-2, 1992.
[26] H.De Man, D. Verkest, K. Van Rompary and I. Bolsens. “Coware – A Design Environment for
Heterogeneous Hardware Software Systems”, Design Automation of Embedded Systems, pp.357-386,
Oct. 1996.
[27] H.De Man, Karl Van Rompaey, Diederik Verkest and Ivo Bolsens. “Coware – A design environment
for heterogeneous hardware software systems”, in Proceedings of the European Design Automation
Conference, pp. 252-257, Sept. 1996.
[28] B.Brandenburg , J. Calandrino, H. Leontyev, A. Block, U. Devi, and J. Anderson, "LITMUSRT: A
Test-bed for Empirically Comparing Real-Time Multiprocessor Schedulers", in Proceedings of the
27th IEEE Real-Time Systems Symposium, pp. 111-123, December 2006.
[29] F.Cerqueira and B. Brandenburg, "A Comparison of Scheduling Latency in Linux, PREEMPT-RT,
and LITMUSRT", in Proceedings of the 9th Annual Workshop on Operating Systems Platforms for
Embedded Real-Time applications, pp. 19-29, July 2013.
[30] A.Parisoto, J. Souza, A., L. Carro, M. Pontremoli, C. Pereira, and A. Suzim, “F-timer: Dedicated
FPGS to real-time systems design support”, in proceeding of 9th Euromicro Workshop on RTS,
Toledo, Spain, pp. 35-40, Jun.1997.
[31] J.Stankovic, W. Burleson, J. Ko, D. Niehaus, K. Ramamritham, G. Wallace and C. Weems, “The
spring scheduling coprocessor: a scheduling accelerator”, in IEEE Transactions on Very Large Scale
Integration Systems, Volume 7, pp. 38-47, Mar. 1999.
[32] J.Hildebrandt, F. Golatowski, and D. Timmermann, “Scheduling coprocessor for enhanced least-
laxity-ﬁrst scheduling in hard real-time systems”, in Proceedings of the 11th Euromicro Conference
on Real-Time Systems, pp. 208-215, 1999.
[33] V.Mooney and D. Blough, “A Hardware/Software Real-Time Operating System Framework for
SOCs”, Design & Test of Computers, IEEE, Volume 19, Issue 6, USA, pp.44-51, Nov. 2002.
[34] V.Mooney and J. Lee. “Hardware/Software Partitioning of Operating Systems: Focus on Deadlock
Detection and Avoidance”, in IEEE Proceeding, Computer and Digital Techniques, UK, pp. 167-182,
July 2005.
[35] V.Mooney and J. Lee, , “RTPOS: A Novel Deadlock Avoidance Algorithm and Its Hardware
Implementation”, in CODES+ISSS '04 Proceedings of the international conference on
Hardware/Software Codesign and System Synthesis, IEEE Computer Society Washington, DC, pp.
200-205, 2004.
[36] V.Mooney III, P. Kuacharoen and M. A. Shalan, “A configurable hardware scheduler for real-time
systems”, in Proceedings of the International Conference on Engineering of Reconfigurable Systems
and Algorithms, CSREA Press , pp. 96-101, 2003.
[37] M.Wirthlin, B. Hutchings, and K. Gilson. “The Nano Processor: a Low Resource Reconfigurable
Processor” in IEEE Workshop on FPGAs for Custom Computing Machines, Napa, CA, pp.23-30,
April 1994.
[38] P.Kohout, B. Ganesh, and B. Jacob, “Hardware support for real-time operating systems”, in
Proceeding of First IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and
System Synthesis (CODES+ISSS 2003), Newport Beach CA, pp. 45-51, Oct. 2003.
[39] M.Vetromille, L. Ost, C. Marcon, C. Reif, and F. Hessel, “RTOS scheduler implementation in
hardware and software for real time applications”, in 17th IEEE International Workshop on Rapid
System Prototyping, pp. 163-168, Jun. 2006.
[40] S.Chandra, F. Regazzoni, and M. Lajolo, “Hardware/software partitioning of operating systems: a
behavioral synthesis approach”, in GLSVLSI ’06 Proceedings of the 16th ACM Great Lakes
symposium on VLSI, (NY, USA), pp. 324-329, ACM, 2006.
[41] Z.Murtaza, S. Khan, A. Raﬁque, K. Bajwa, and U. Zaman, “Silicon real time operating system for
embedded DSPs”, in ICET’ 06: Proceedings of International Conference on Emerging Technologies,
(Peshwar), IEEE, pp. 188-191, Nov. 2006.

12
[42] M.Song, S. H. Hong, and Y. Chung, “Reducing the overhead of real-time operating system through
reconfigurable hardware”, in proceedings of 10th Euromicro Conference on Digital System Design
Architectures, Methods and Tools, pp. 311-316, Aug. 2007.
[43] A.S.R.Oliveira, L. Almeida, and A. B. Ferrari, “The ARPA-MT embedded SMT processor and its
RTOS hardware accelerator”, Industrial Electronics, IEEE Transactions on, Volume 58, No. 3, pp.
890-904, March 2011.
[44] Luis Almeida, A. S. R. Oliveira and Antonio B. Ferrari. “A specialized and predictable processor for
real-time systems”, in Workshop on Application Speciﬁc Processors, pp. 32-38, Nov. 2009.
[45] Luis Almeida, Nelson Silva, Arnaldo Oliveira and Rui Santos. "The OReK real-time micro kernel for
FPGA-based systems-on-chip", in proceedings of 6th Workshop on Embedded Systems for Real-time
Multimedia, (ESTImedia 2008), IEEE Xplore, Atlanta Georgia, pp. 75-80, Oct. 2008.
[46] Xiangrong Zhou, Peter Petrov “Rapid and low-cost context-switch through embedded processor
customization for real-time and control applications” DAC San Francisco, CA, pp. 352-357, July
2006.
[47] N. Maruyama, T. Ishihara, and H. Yasuura, “An RTOS in hardware for energy efficient software-
based TCP/IP processing”, in IEEE Symposium on Application Specific Processors, pp. 58-63, June
2010.
[48] Hayden Kwok-Hay So, Xun Changqing, Wen Mei, Wu Nan, Zhang Chunyuan. “Extending BORPH
for shared memory reconfigurable computers Field Programmable Logic and Applications (FPL)” in
22nd International Conference on IEEE Improving Usability of FPGA-Based Reconfigurable
Computers Through Operating System Support, Oslo. pp. 563-566, Aug. 2012.
[49] Hyden Kwok-Hay So, Robert W. Broderson “BORPH: An Operating System for FPGA-Based
Reconfigurable Computers” DAC University of California, Berkeley, Technical Report No.
UCB/EECS, pp. 92-96, July 2007.
[50] Lange, A.B. “Hardware RTOS for FPGA based embedded systems”, Master's thesis, University of
Southern Denmark. http://www.hartos.dk/publications/thesis/hartos.pdf accessed on Nov.2015.
[51] Kohout, P., Ganesh B. and Jacob B. “Hardware support for Real-Time Operating Systems”, in
Proceedings of the First IEEE/ACM/IFIP International Conference on HW/SW co-design and System
Synthesis, pp.45-51, 2003.
[52] D. G. Harkut & M.S.Ali, “Hardware Support for Adaptive Task Scheduler in RTOS”, Intelligent
Systems Technologies & Applications, Volume 384, Springer, UK, pp. 227-245, 2015.
[53] Sabeghi M., Naghibzadeh M., Taghavi T., “Scheduling Non-Preemptive Periodic Task in Soft Real-
time Systems using fuzzy Inference”, 9th IEEE International Symposium on Object and component-
oriented Real-Time distributed Computing(ISORC), April 2006.
[54] Mahdi Hamzeh, Sied Mehdi Fakhraie, and Caro Lucas, "Soft real-time fuzzy task scheduling for
multiprocessor systems", International journal of intelligent technology Vol. 2 No. 4, pp. 211-216,
2007.
[55] Mamdami E. H., Assilian S., “An experiment in linguistic synthesis with a fuzzy logic controller ”, in
International Journal of Man-Machine Studies, Vol.7,No.1, pp. 1-13, 1975.
[56] Jang,J.S.R., “ANFIS: Adaptive-Network-based Fuzzy Inference Systems”, IEEE Transactions on
Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685,1993.
[57] Lindh,L., Stärner, J. and Furunäs, J. “From Single to Multiprocessor Real-Time Kernels in
Hardware”, in IEEE Real Time Technology and Applications Symposium. Chicago, May 1995.
[58] Furunäs,J., Adomat, J., Lindh, L., Stärner, J. and Vörös, P. “A Prototype for Interprocess
Communication Support, in Hardware in Real-Time Systems”, in Proceedings of EUROMICRO
Workshop, Toledo, Spain, pp. 18-24, June 1997.
[59 ]Lindh, L. “A Real-Time Kernel implemented in one chip”, in IEEE press, Real-Time Workshop ,
Oulu, June 1993.
[60] Adomat,J., Furunäs, J., Lindh, L. and Stärner, J. “Real-Time Kernel in Hardware RTU: A Step
Towards Deterministic and High-Performance Real-Time Systems”, in Proceedings of the Euromicro
Workshop on Real-Time Systems, L’Aquila, Italy, June 1996.
[61] Lindh,L. “Utilization of Hardware Parallelism in Realizing Real Time Kernels”, Doctoral Thesis,
TRITA – TDE 1994:1, ISSN 0280-4506, ISRN KTH/TDE/FR-94/1-SE, Department of Electronics,
Royal Institute of technology, Stockholm, Sweden, 1994, accessed on Nov.2015.

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AUTHORS
Dinesh G Harkut received B.E.(Computer Science & Engineering) & M.E. (Computer
Science & Engineering) from SGB Amravati University in 1991 and 1998 respectively. He
completed his masters in Business Management and obtained his Ph.D. from SGB Amravati
University in Business Management in 2013 while serving as a full-time faculty in the Dept.
of Computer Science & Engineering at Prof Ram Meghe College of Engineering &
Management, Badnera – Amravati. His research interests are Embedded Systems and RTOS.
Dr. M. S. Ali is a Professor and Principal of Prof Ram Meghe College of Engineering &
Management, Badnera – Amravati. He obtained his B.E.(Electronics & Power) and
M.Tech.(Power Electronics) from Nagpur University and I.I.T. Powai, Mumbai in 1981 &
1984 respectively He obtained his Ph.D. from SGB Amravati University in 2006. He has
been on the SGB University’s various body like Board of Studies, Faculty of Engineering &
Technology and Academic Council since last fifteen years. He is Hon’ble Chancellors nominee on the
senate of RTM Nagpur University. His research interests are Operating Systems, Artificial Intelligence and
Java Technologies.

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