SysPy: using Python for processor-centric SoC design

Logaras, Evangelos; Μανωλάκος, Ηλίας Σ.

doi:10.1109/icecs.2010.5724624

Cited by 14 publications

(6 citation statements)

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“…Dictionaries can be very useful in storing design or signal parameters, thus creating easy-to-read hardware descriptions. Examples of behavioral and structural Python hardware descriptions using SysPy can be found in an earlier publication [Logaras and Manolakos 2010].…”

Section: Hardware Modules Descriptionmentioning

confidence: 99%

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Python to accelerate embedded SoC design

Logaras

Hazapis

Μανωλάκος

2014

ACM Trans. Embed. Comput. Syst.

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We present SysPy (System Python) a tool which exploits the strengths of the popular Python scripting language to boost design productivity of embedded System on Chips for FPGAs. SysPy acts as a "glue" software between mature HDLs, ready-to-use VHDL components and programmable processor soft IP cores. SysPy can be used to: (i) automatically translate hardware components described in Python into synthesizable VHDL, (ii) capture top-level structural descriptions of processor-centric SoCs in Python, (iii) implement all the steps necessary to compile the user's C code for an instruction set processor core and generate processor specific Tcl scripts that import to the design project all the necessary HDL files of the processor's description and instantiate/connect the core to other blocks in a synthesizable top-level Python description. Moreover, we have developed a Hardware Abstraction Layer (HAL) in Python which allows user applications running in a host PC to utilize effortlessly the SoC's resources in the FPGA. SysPy's design capabilities, when complemented with the developed HAL software API, provide all the necessary tools for hw/sw partitioning and iterative design for efficient SoC's performance tuning. We demonstrate how SysPy's design flow and functionalities can be used by building a processor-centric embedded SoC for computational systems biology. The designed SoC, implemented using a Xilinx Virtex-5 FPGA, combines the flexibility of a programmable soft processor core (Leon3) with the high performance of an application specific core to simulate flexibly and efficiently the stochastic behavior of large size biomolecular reaction networks. Such networks are essential for studying the dynamics of complex biological systems consisting of multiple interacting pathways.

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Section: Hardware Modules Descriptionmentioning

confidence: 99%

“…In Logaras and Manolakos [2010] we presented an image processing SoC that was designed using SysPy around an AVR 8-bit processor core. Since then SysPy has evolved along the following important directions: a) Supports a structured way to communicate …”

Section: Soc Design Examples With Syspy For Systems Biologymentioning

confidence: 99%

Python to accelerate embedded SoC design

Logaras

Hazapis

Μανωλάκος

2014

ACM Trans. Embed. Comput. Syst.

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“…While using C++/Python have many advantages in the design phase of the NN algorithm including well documented library for Python and ease of implementation, there are few challenges in moving from these languages to microcontroller code. Python requires lot of memory for execution and uses fair amount of power consumption, which are limiting factors for microcontroller codes [32]. This is especially true when using popular packages such as numpy and scipy.…”

Section: Implementation In Mobile Devices For Real-time Monitoringmentioning

confidence: 99%

Identification and quantification of gases and their mixtures using GaN sensor array and artificial neural network

Khan

Motayed

Rao

2021

Meas. Sci. Technol.

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Accurate identification and quantification of gas mixtures are almost unattainable utilizing only a metal-oxide/GaN sensor because of its cross-sensitivity to many gases. Here, an array of sensors has been formed consisting of Ag and Pt incorporated ZnO, In2O3 and TiO2 coated two terminal GaN photoconductors. The common environmental toxic gases, such as SO2, NO2, H2, ethanol and their mixtures have been chosen as the gas analytes. All the gas responses have been obtained at 20 °C under UV illumination. Temporal responses have been post-processed to develop the training and test dataset. Then, four different artificial neural network models have been analyzed and optimized for gas classification study, which is done for the first time on GaN sensors. Statistical and computational complexity results indicate that back-propagation neural network (NN) stands out as the optimal classifier among the considered algorithms. Then, ppm concentrations of the identified gases have been estimated using the optimal model. Furthermore, implementation of the developed sensor array in combination with NN algorithm for real-time gas monitoring applications has been discussed.

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“…PyRTL [33] [34] , PyMTL [35] , SysPy [36] 和 PHDL [37] 等, 这些语言针对不同的应用目标, 利用 Python 图 5 定义的元素及 AST 表示示例 [14] 图 6 FIRRTL 中间格式的处理 [14] 它设计了完备的硬件元语, 大量采用位向量数据类图 7 以 CoreIR 为核心的敏捷设计流程 [39] 3.3 微处理器生成技术微处理器生成器的核心思想是避免设计单个芯片实例, 而是通过设计生成器, 配置参数, 自动生成满足需求的微处理器 [1] , 其关键技术是微处理器特征参数的建模、模板电路、模板体系结构的设计, 这些技术涉及的内容常常与应用领域密切相关.…”

Section: 敏捷设计方法关键使能技术unclassified