Sensor Signal Processing using High-Level Synthesis and Internet of Things with a Layered Architecture

International Journal on AdHoc Networking Systems (IJANS) Vol. 12, No. 4, October 2022
DOI: 10.5121/ijans.2022.12403 35
SENSOR SIGNAL PROCESSING USING HIGH-LEVEL
SYNTHESIS AND INTERNET OF THINGS WITH A
LAYERED ARCHITECTURE
CS Reddy1
and Krishna Anand2
1
Department of Mathematics, CIT - NC, VTU University, Bangalore, India
2
Department of Computer Engineering, Anurag University, Hyderabad, India
ABSTRACT
Sensor routers play a crucial role in the sector of Internet of Things applications, in which the capacity for
transmission of the network signal is limited from cloud systems to sensors and its reversal process. It
describes a robust recognized framework with various architected layers to process data at high level
synthesis. It is designed to sense the nodes instinctually with the help of Internet of Things where the
applications arise in cloud systems. In this paper embedded PEs with four layer new design framework
architecture is proposed to sense the devises of IOT applications with the support of high-level synthesis
DBMF (database management function) tool.
KEYWORDS
Network Protocols, Wireless Network, Mobile Network, Internet of Things, Reconfigurable dynamic
processor, Sensor signal processing.
1. INTRODUCTION
Sensor routers play a crucial role in the sector of Internet of Things (IOT) applications, in which
the capacity for transmission of the network signal is limited from cloud systems to sensors and
its reversal process. Consequence to this volume of the data reduction is obligatory to combat
device computing functions between sensor nodes and transmitter to exchange the sufficient data
with the available networks [1-3]. Hence, low power consumption and small footprints are
commanded among sensor nodes to process information. One of the replacements for
microcontroller units is field programmable gate arrays to optimize the footprints size, so that it is
to be observed keenly the routing with configurable logic blocks and switches of look-up tables
which causes placement inefficiency [4, 7]. To fabricate Field Programmable Gate Arrays
(FPGA) it is wise to use High-level synthesis which will enable global optimization and
recompense the limitation of Routing and Placement [5, 6].
In this paper embedded PEs with four layer new design framework architecture is proposed to
sense the devises of IOT applications with the support of high-level synthesis DBMF (database
management function) tool [8, 17, 19]. It exploits the repetitive high level synthesis process.
Macro blocks synthesized through high-level behavioural synthesis are registered in a database
before the system level synthesis, and the information in the database is used for the optimization
of resource consumption through the system level synthesis [10]. In this work authors tried to
investigate the dependencies of resource consumption on the granularity of coarse grained
function definitions using the extended database management function of Cyber Work Bench.
The evaluation results show that small footprint was achieved especially with dynamically
reconfigurable technique [9, 15].

36
Dynamically reconfigurable processors using high-level synthesis were proposed to improve the
efficiency, whereas the inefficiency of fixed mesh pointed out in still remains [11-14]. Fixed bit
widths of data paths, elementary blocks, and switch matrices aiming at mass production of the
devices were one example of the inefficiency. Sensors used in IOT applications have various data
interfaces, such as 8, 12, 14, or 16 bits [ 17, 19]. Predefined data path between arrays of
arithmetic logic units prevents behavioural synthesis tools from the optimization of layout size
and the reduction in power consumption. Therefore, optimized Arithmetic Logic Units (ALU)
and flexible data paths are required to embed processors in sensor units [13, 15-17].
2. HIGH-LEVEL SYNTHESIS TOOLS
High-level synthesis is increasingly popular for the design of high-performance and energy-
efficient heterogeneous systems, shortening time-to-market and addressing today’s system
complexity [18, 20, 25]. Early academic work extracted scheduling, allocation, and binding as the
basic steps for high-level-synthesis [22, 24, 32]. Scheduling partitions in the algorithm to control
steps that are used in the model are defined the states in the finite-state machine [21, 33].
First generation behavioural synthesis was introduced by Synopsys in 1994 as behavioural
Compiler and used Verilog or VHDL as input languages. 10 years later, in 2004, there emerged a
number of next generation commercial high-level synthesis products which provided synthesis of
circuits specified at C level to a register transfer level specification [23, 25, 26]. It was primarily
adopted in Japan and Europe in the early years. As of late 2008, there was an emerging adoption
in the United States. High-level synthesis (HLS) allows designers to work at a higher level of
abstraction by using a software program to specify the hardware functionality [28, 29].
Additionally, HLS is particularly interesting for designing field-programmable gate array circuits,
where hardware implementations can be easily refined and replaced in the target device [27, 30-
32]. Recent years have seen much activity in the HLS research community, with a plethora of
HLS tool offerings, from both industry and academia.
Fig.1. Classification of High-Level Synthesis Input Language.

37
HLS tools start from a software programmable high-level language to automatically produce a
circuit specification in HDL that performs the same function. The most common source inputs for
high-level synthesis are based on standard languages such as ANSI C/C++, System C and
MATLAB. HLS has also been recently applied to a variety of applications (e.g., medical
imaging, convolutional neural networks, and machine learning), with significant benefits in terms
of performance and energy consumption.
These HLS tools can leverage dedicated optimizations or micro architectural solutions for the
specific domain. However, the algorithm designer, who is usually a software engineer, has to
understand how to properly update the code. This approach is usually time-consuming and error
prone. For this reason, some HLS tools offer complete support for a standard HLL, such as C,
giving complete freedom to the algorithm designer. High level languages mainly classified into
two types that are shown in Fig. 1. One is Domain specific languages and other one is Generic
languages. In Domain specific languages it consist new languages and C- extended languages,
whereas, in Generic languages it consist Procedural and Object Oriented languages. HLS tool
select specific language for specific applications.
2.1. Five Key Challenges
In this section the important crucial challenges that arise in the process of signal synthesis in the
layered architecture.
 Hardware in inherently concurrent, whereas software representations are sequential. HLS
must map the sequential algorithm onto concurrent hardware.
 Timing is implicit in software in the sequence of instructions used. Synchronous hardware
must deal with timing constraints, with controlling and synchronizing operations at the clock
cycle level.
 In software, the word length is fixed (8, 16, 32 or 64bits), but in hardware, it is usually
optimized for the task being performed.
 The software model of memory is as a single block with a monolithic address space, with
almost all data items stored in memory. On an FPGA, local variables are stored in registers,
with multiple distributed memory blocks, each with their own independent address space. In
such an environment, pointers have little meaning, and dynamic memory allocation is very
difficult. Communication in software is usually through shared memory, whereas on an
FPGA it relies on constructing appropriate hardware, from implicit (within stream
processing), to simple token passing, to using dedicated FIFOs to manage flow control.
3. DATA LAYER INTERACTION ARCHITECTURE
Traditionally, the word architecture is concerned with the art or science of building, where it
relates to structural concepts and to styles of design. Here the authors will use it in the sense of
structured deceptions of a system from a number of compatible and complementary viewpoints
which, taken together, cover functional, design, fabrication and performance issues. The term
layer is used to refer to a particular descriptive viewpoint. Within a layer, descriptions will
generally be hierarchic to allow the containment of complexity or the exposure of detail by
suitable aggregation or decomposition techniques. Architecture(s) may be singular or plural
depending on whether it will be addressed as an individual layer or the complete set of
descriptions across all layers. It is clear from this definition that the architecture of a system is not
just confined to some sort of functional partitioning at the front end of a development, but
essentially embraces descriptions generated during all phases and stages of the development
process.

38
The Data Interaction Architecture is a prime example of a layered architecture. It contains four
layers, each of which can be regarded as a model of the system from a particular viewpoint.
Fig.2. shows some small representational fragments for each of the four layers. The layers consist
of I/O circuitry, fine grained layer, coarse grained function definition layer, and bypass
connection layer, where they are listed from the bottom layer to the top layer, respectively. Well
defined notations and technical conventions exist for each of these layers and are closely
modelled on those of MASCOT, Modular Approach to Software Construction Operation and
Test. Where relevant the notations include composite structures to support hierarchical
representations over multiple levels within a layer. The motivation behind the layered
architecture method is to provide the means of moving from a functional model of a system to an
execution model.
Fig.2. Data Interaction Architecture
The emphasis on the identification of well-defined interactions in the functional model, and the
preservation of these speck tied interaction properties through the subsequent layers, provides the
structural conformance by which traceability is achieved. It follows that the functional model is
binding on subsequent development. Developers are not free to reverse engineer the functional
model to allow an alternative development path in which traceability would be lost. Of course the
functional model may need to be changed, either because system requirements have changed, or
because detail design has shown that an alternative functional model would be better. Whatever
the reason for change, consistency across the model set must be preserved.
The main distinguishing feature of Data Interaction Architecture (DIA) is the explicit
representation of shared information and shared data. Here i will concentrate on the sharing

39
implicit in the bilateral interactions between processes, leaving discussion of the more general
information retention element. A summary description of the model in each layer shows how
bilateral interaction properties are tracked.
4. ARCHITECTURE OF PROCESSING ELEMENTS
In this paper authors proposed to establish a new design framework to solve issues described in
the preceding section by exploiting the binding process of high-level/behavioural synthesis.
4.1. Layered Architecture
The purpose of the conventional high-level synthesis tools is to generate finite state machines
with data paths. Here File System Meta-Data (FSMD) referred as fine grained layer. It is
expanded with the scope of high-level synthesis to coarse grained layer to exploit the functional
representations of circuitries. ALUs in coarse grained layer are defined by the functional
representations of high-level programming languages. Switches are added as bypass connection
layer for the scope of high-level synthesis intentionally. Implicit as-built meshes of switches put
constraints on high-level synthesis. In the processes are allowed as unprejudiced bypass switches
to remove the meshes. Communications between the PEs are limited between adjacent PEs,
whereas it is found no limitations for sensor applications with the limited communication paths.
The following explains these layers I/O circuitry is implemented with random logic gates and
mixed signal I/O circuitries. They are connected to the fine grained blocks in the above layer. The
fine grained layer mainly consists of finite state machines and data paths. They are replaceable to
follow the context described by high-level languages. The coarse grained function definition
layer is located on the fine grained layer. Optimized ALUs for a certain application are defined in
this layer. An operation primitive defined in the layer corresponds to an operation code (op code)
of a conventional multipoint control unit (MCU), whereas it does not have to be standardized for
over many applications. As for the topmost bypass connection layer, simple bypass switch
images can be specified over coarse grained blocks. The connections between inputs and outputs
of PEs are configurable with the bypass switches.
4.2. Design Flow
The following six steps compose the design flow of the design framework to design a PE through
layered scheme.
Step 1: Describe a system in high-level language.
Step 2: Define coarse grained operations. Typical dedicated functions for sensor signals are
signal compensation, feature point identification for image recognition, and image
compression in addition to basic arithmetic operations. The defined coarse grained
operations are exploited in high-level synthesis and behavioural synthesis process.
Step 3: Generate source codes written in hardware description language through behavioural
synthesis. The hard macros defined and implemented in the step 2 are exploited for
generating circuitries by the functions of CWB.
Step 4: This step is identical to conventional logic synthesis.
Step 5: This step is identical to conventional layout design.
Step 6: The verification and validation step include back annotation based on the result of delay
analysis.

40
In the work authors used a data base management function of CWB to exploit the definitions of
operations in the coarse grained layer during the behavioural synthesis to treat a function as an
operator. The operations can be implemented as hard macros by using custom ASIC design tools
and/or LUTs of FPGAs. The operator definitions were exploited on the step 3 as macro blocks.
Once macro blocks are registered in a database, CWB can use the macro blocks during the
binding process of high-level synthesis automatically to reduce layout area size. The binding
process of high-level synthesis is regarded as nondeterministic polynomial (NP) hard and
heuristic approach was often employed. The design flow enables the automation of binding
process instead of the heuristic approach.
4.3. Typical Design of Processing Element
Two types of context were identified with reference to semantics or lambda calculus of functional
representations to define ALUs in coarse grained function definition layer. One is a configurable
static context often mentioned as stored information in a file, and the other is a dynamic context
as stored information in heap registers as shown in Fig. 3. The static contexts of an application
are expressed with finite state machines and data paths implemented by n sets of hardware
circuitry of PEs. The dynamic contexts of an application are specified as m sets of registers and
instructions. The instruction sets are optimized for an application, and each optimized instruction
set can be shared among some dynamic contexts.
Fig.3. Modified High-Level Synthesis Process.
The program code is implemented with a specific C-language comment description /* Cyber func
= operator */ for defining dynamic and static contexts, which are the sets of pairs of registers and
optimized instruction sets. The pair of a register set and an instruction set can be shared among
dynamic contexts using another specific C-language comment description as /* Cyber share name
= NAME */. NAME is an arbitrary designation. The binding process of high-level behavioural

41
synthesis can be controlled by these descriptions to exploit the functional representations defined
in a high-level programming language.
5. RESULTS
Experiments are carried out to evaluate the design framework using convolution operations; those
are often used for sensor applications, with following three conditions. The matrix functions of
the filters are similar and the difference is the parameters of 3×3 matrices. It is observed one can
evaluate the layout area size reduction results by using an FPGA, Xilinx XC7A200T FPGA,
although the design framework was established aiming at improving ASIC design at first.
 Defining a convolution function as one operator.
 Implementing ALUs with basic operations, i.e., plus, minus, multiply, divide, and
comparison operations, using dynamically reconfiguration technique designated as
flexible reliability reconfigurable array (FRRA).
 Elaborating whole design with FPGA libraries without operator definitions and a
function definition database.
Fig.4. Comparison of LUT utilizations According to Operator Definitions
Derived LUT counts for each evaluation case are shown in Fig.4. The LUT counts of basic
operations were excluded for comparison. The larger the granularity of an operation was, the less
counts of LUTs were consumed. Remarkable difference of LUT counts was shown for cascaded
operations. The LUT counts of cascaded operations using FPGA library was more than double
compared to single operation. The results show that exploiting granularity in behavioural
synthesis was carried out by CWB automatically without specifying the reuse of macro blocks
explicitly. The difference between one operation and cascaded operations using FRRAs with
dynamically reconfiguration technique was 22% less than the difference using the operator
definition on convolution operations.
6. CONCLUSIONS
A novel model is developed to design a framework to reduce the footprints of programmable
functions of sensing devices for IOT applications. The design framework consists of four layered
structure of PE architecture and the extended database management function of a high-level
synthesis tool to exploit functional representations in high-level programming languages.

42
The layout size reduction is useful to embed a PE into a sensing device and to provide device
computing capabilities with a sensing device. The experimental results report the power
consumption reduction by adopting the design framework on a Nano Bridge FPGA.
REFERENCES
[1] Hihara, H., Iwasaki, A., Hashimoto, M., Ochi, H., Mitsuyama, Y., Onodera, H., ... & Sakamoto, T.
(2018). “Sensor signal processing using high-level synthesis with a layered architecture”, IEEE
Embedded Systems Letters, 10(4), 119-122.
[2] Sreelatha, V., Mamatha, E., Reddy, C. S., & Rajdurai, P. S. (2022). “Spectrum Relay Performance of
Cognitive Radio between Users with Random Arrivals and Departures,” In Mobile Radio
Communications and 5G Networks (pp. 533-542). Springer, Singapore.
[3] R. Zhao, M. Tan, S. Dai, and Z. Zhang, “Area-efficient pipelining for FPGA-targeted high-level
synthesis,” in Proc. 52nd ACM/EDAC/IEEE Design Autom. Conf. (DAC), San Francisco, CA, USA,
2015.
[4] Saritha, S., Mamatha, E., Reddy, C. S., & Rajadurai, P. (2022). “A model for overflow queuing
network with two-station heterogeneous system,” International Journal of Process Management and
Benchmarking, 12(2), 147-158.
[5] Mamatha, E., Anand, S. K., Devika, B., Prasad, S. T., & Reddy, C. S. (2021, July). “Performance
Analysis of Data Packets Service in Queuing Networks System,” In 2021 12th International
Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-4).
IEEE.
[6] M. Gort and J. Anderson, “Design re-use for compile time reduction in FPGA high-level synthesis
flows,” in Proc. Int. Conf. Field Program. Technol. (FPT), Shanghai, China, 2014, pp. 4–11.
[7] Saritha, S., Mamatha, E., Reddy, C.S., Anand, K. (2019). “A model for compound poisson process
queuing system with batch arrivals and services,” Journal Européen des Systèmes Automatisés, Vol.
53, No. 1, pp. 81-86.
[8] M. Schmid, O. Reiche, C. Schmitt, F. Hannig, and J. Teich, “Code generation for high-level synthesis
of multi resolution applications on FPGAs,” in Proc. FSP, 2014, pp. 21–26.
[9] Mamatha, E., et al. "Mathematical modelling and performance analysis of single server queuing
system-eigenspectrum," International Journal of Mathematics in Operational Research 16.4 (2020):
455-468.
[10] Saritha, S., Mamatha, E., Reddy, C.S. (2019). “Performance measures of online warehouse service
system with replenishment policy. Journal Européen des Systèmes Automatisés,” Vol. 52, No. 6, pp.
631-638
[11] D. Alnajjar et al., “Reliability-configurable mixed-grained reconfigurable array supporting C-to-array
mapping and its radiation testing,” in Proc. IEEE A-SSCC, 2013, pp. 313–316.
[12] Mamatha, E., C. S. Reddy, and Rohit Sharma. "Effects of viscosity variation and thermal effects in
squeeze films." Annales de Chimie. Science des Materiaux. Vol. 42. No. 1. Lavoisier, 2018.
[13] Kumar, MV Sravan, et al. "Data hiding with dual based reversible image using sudoku technique."
2017 International Conference on Advances in Computing, Communications and Informatics
(ICACCI). IEEE, 2017.
[14] Mamatha, E., C. S. Reddy, and S. Krishna Anand. "Focal point computation and homogeneous
geometrical transformation for linear curves." Perspectives in Science 8 (2016): 19-21.
[15] Mamatha, E., Reddy, C. S., & Prasad, R. (2012). “Mathematical modeling of markovian queuing
network with repairs, breakdown and fixed buffer.” i-Manager's Journal on Software Engineering,
6(3), 21.
[16] Anand, Krishna, et al. "Design of neural network based expert system for automated lime kiln
system." Journal Européen des Systèmes Automatisés 52.4 (2019): 369-376.
[17] F. Wang, G. T. Wang, R. H. Wang, and X. W. Huang, “FPGA implementation of Laplacian of
Gaussian edge detection algorithm,” Adv. Mater. Res., vols. 282–283, pp. 157–160, Jul. 2011.
[18] Elliriki, M., Reddy, C. S., Anand, K., & Saritha, S. (2021). “Multi server queuing system with crashes
and alternative repair strategies.” Communications in Statistics-Theory and Methods, 1-13.
[19] Y. Hasegawa et al., “Design methodology and trade-offs analysis for parameterized dynamically
reconfigurable processor arrays,” in Proc. FPL, Amsterdam, The Netherlands, 2007, pp. 796–799.

43
[20] T. Toi et al., “High-level synthesis challenges and solutions for a dynamically reconfigurable
processor,” in Proc. IEEE/ACM Int. Conf. Comput. Aided Design, San Jose, CA, USA, 2006, pp.
702–708.
[21] Mamatha, E., C. S. Reddy, and K. R. Prasad. "Antialiased Digital Pixel Plotting for Raster Scan Lines
Using Area Evaluation." Emerging Research in Computing, Information, Communication and
Applications. Springer, Singapore, 2016. 461-468.
[22] K. Wakabayashi, “Cyber Workbench: Integrated design environment based on C-based behavior
synthesis and verification,” in Proc. IEEE VLSI-TSA Int. Symp., H sinchu, Taiwan, 2005, pp. 173–
176.
[23] Mamatha, E., S. Saritha, and C. S. Reddy. "Stochastic Scheduling Algorithm for Distributed Cloud
Networks using Heuristic Approach." International Journal of Advanced Networking and
Applications 8.1 (2016): 3009.
[24] Reddy, C. S., et al. "Obtaining Description for Simple Images using Surface Realization Techniques
and Natural Language Processing." Indian Journal of Science and Technology 9.22 (2016): 1-7.
[25] M. Meribout and M. Motomura, “A-combined approach to highlevel synthesis for dynamically
reconfigurable systems,” IEEE Trans. Comput., vol. 53, no. 12, pp. 1508–1522, Dec. 2004.
[26] R. Hartenstein, “The microprocessor is no longer general purpose: Why future reconfigurable
platforms will win,” in Proc. 2nd Annu. IEEE Int. Conf. Innov. Syst. Silicon, Austin, TX, USA, 1997,
pp. 2–12.
[27] Elliriki, M., Reddy, C. C. S., & Anand, K. (2019). “An efficient line clipping algorithm in 2D space.”
Int. Arab J. Inf. Technol., 16(5), 798-807.
[28] L. Chen, “Fast generation of Gaussian and Laplacian image pyramids using an FPGA-based custom
computing platform,” M.S. thesis, Virginia Polytechnic Inst. State Univ., Blacksburg, VA, USA,
1994.
[29] Raw, R. S., & Lobiyal, D. K. (2012). Throughput and delay analysis of next-hop forwarding method
for non-linear vehicular ad hoc networks. International Journal on Ad Hoc Networking Systems, 2(2),
33-44.
[30] Mamatha, E., Sasritha, S., & Reddy, C. S. (2017). “Expert system and heuristics algorithm for cloud
resource scheduling.” Romanian Statistical Review, 65(1), 3-18.
[31] H. Hihara et al., “Novel processor architecture for onboard infrared sensors,” in Proc. SPIE Infrared
Remote Sens. Instrum. XXIV, vol. 9973, San Diego, CA, USA, Sep. 2016, Art. no. 99730S.
[32] Abdelgadir, M., Saeed, R. A., & Babiker, A. (2018). Cross layer design approach for efficient data
delivery based on IEEE 802.11 P in vehicular ad-hoc networks (VANETS) for city scenarios.
International Journal on Ad Hoc Networking Systems (IJANS), 8(4), 01-12.
[33] Beritelli, F., La Corte, A., Rametta, C., & Scaglione, F. (2015). A Cellular bonding and adaptive load
balancing based multi-sim gateway for mobile ad hoc and sensor networks. International Journal on
Ad Hoc Networking Systems (IJANS), 5(3).

Sensor Signal Processing using High-Level Synthesis and Internet of Things with a Layered Architecture

More Related Content

Similar to Sensor Signal Processing using High-Level Synthesis and Internet of Things with a Layered Architecture (20)

More from pijans (20)

Recently uploaded (20)

Sensor Signal Processing using High-Level Synthesis and Internet of Things with a Layered Architecture