HARDWARE IMPLEMENTATION OF STOCHASTIC SPIKING NEURAL NETWORKS

2012 ◽  
Vol 22 (04) ◽  
pp. 1250014 ◽  
Author(s):  
JOSEP L. ROSSELLÓ ◽  
VINCENT CANALS ◽  
ANTONI MORRO ◽  
ANTONI OLIVER
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Hardware Implementation ◽  
Linear Equations ◽  
Spiking Neural Networks ◽  
Proposed Model ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Systems Of Linear Equations ◽  
Probabilistic Nature

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

scholarly journals HARDWARE IMPLEMENTATION OF AN ENCRYPTION FOR ENHANCEMENT DGHV

2019 ◽  
Vol 2 (2) ◽  
pp. 44-57
Author(s):  
Zainab H. Mahmood ◽  
Mahmood K. Ibrahem
Keyword(s):  
Real Time ◽  
High Speed ◽  
Hardware Implementation ◽  
Graphics Processing Unit ◽  
Homomorphic Encryption ◽  
Processing Unit ◽  
Fully Homomorphic Encryption ◽  
Encryption Schemes ◽  
Field Programmable ◽  
Programmable Gate Arrays

In constructing a secure and reliable cloud computing environment, a fully homomorphic encryption (FHE) scheme is conceived as a major cryptographic tool, as it enables arbitrary arithmetic evaluation of a cipher text without revealing the plaintext. However, due to very high of fully homomorphic encryption systems stays impractical and unfit for real-time applications  One way to address this restriction is by using graphics processing unit (GPUs) and field programmable gate arrays (FPGAs) to produce homomorphic encryption schemes. This paper represents the hardware implementation of an encryption for enhancement van Dijk, Gentry, Halevi and Vaikuntanathan’s (DGHV) scheme over the integer (DGHV10) using FPGA technology for high speed computation and real time results. The proposed method was simulated via Vivado system generator tools. Then design systems of fully homomrphic encryption are implemented in an FPGA hardware successfully using NEXYS 4 DDR board with ARTIX 7 XC7A100T FPGA. The Experimental results show that the FPGA- based fully homomorphic encryption system is 63 times faster than the simulation based implementation.

scholarly journals Design and Implementation of MIPS Processor for LU Decomposition based on FPGA

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Rusul Saad Khalil ◽  
Safaa S. Omran
Keyword(s):  
Integrated Circuit ◽  
High Speed ◽  
Linear Equations ◽  
Lu Decomposition ◽  
Gate Arrays ◽  
The Matrix ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Hardware Description ◽  
Lower Triangle

The solution for a set of liner equations require to find the matrix inverse of a square matrix with same number of the linear equations, this operation require many mathematical calculations. To solve this problem, LU decomposition for the matrix is used, which computes two matrices, a lower triangle matrix and an upper triangle matrix. In this, paper a design for 32-bits MIPS (microprocessor without interlocked pipelined stages) processor with the required instructions that used to calculate the LU matrices. The design implemented using VHDL (Very high speed integrated circuit hardware description language) then integrated with FPGA (Field Programmable Gate Arrays) Xilinx Spartan 6. The results for the different parts of the processor are resented in the form of test bench waveform and the architecture of the system is demonstrated and the results was matched with theoretical results.

scholarly journals AC_ICAP: A Flexible High Speed ICAP Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Luis Andres Cardona ◽  
Carles Ferrer
Keyword(s):  
High Speed ◽  
Access Port ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Speed Up ◽  
Run Time ◽  
Ip Cores ◽  
Similar Solutions ◽  
Internal Configuration ◽  
Run Time Reconfiguration

The Internal Configuration Access Port (ICAP) is the core component of any dynamic partial reconfigurable system implemented in Xilinx SRAM-based Field Programmable Gate Arrays (FPGAs). We developed a new high speed ICAP controller, named AC_ICAP, completely implemented in hardware. In addition to similar solutions to accelerate the management of partial bitstreams and frames, AC_ICAP also supports run-time reconfiguration of LUTs without requiring precomputed partial bitstreams. This last characteristic was possible by performing reverse engineering on the bitstream. Besides, we adapted this hardware-based solution to provide IP cores accessible from the MicroBlaze processor. To this end, the controller was extended and three versions were implemented to evaluate its performance when connected to Peripheral Local Bus (PLB), Fast Simplex Link (FSL), and AXI interfaces of the processor. In consequence, the controller can exploit the flexibility that the processor offers but taking advantage of the hardware speed-up. It was implemented in both Virtex-5 and Kintex7 FPGAs. Results of reconfiguration time showed that run-time reconfiguration of single LUTs in Virtex-5 devices was performed in less than 5 μs which implies a speed-up of more than 380x compared to the Xilinx XPS_HWICAP controller.

scholarly journals Pattern Classification of Fabric Defects Using a Probabilistic Neural Network and Its Hardware Implementation using the Field Programmable Gate Array System

2017 ◽  
Vol 25 (0) ◽  
pp. 42-48 ◽  
Author(s):  
Abul Hasnat ◽  
Anindya Ghosh ◽  
Amina Khatun ◽  
Santanu Halder
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Probabilistic Neural Network ◽  
Clock Period ◽  
Data Set ◽  
Gate Arrays ◽  
Hardware System ◽  
Field Programmable ◽  
Programmable Gate Arrays

This study proposes a fabric defect classification system using a Probabilistic Neural Network (PNN) and its hardware implementation using a Field Programmable Gate Arrays (FPGA) based system. The PNN classifier achieves an accuracy of 98 ± 2% for the test data set, whereas the FPGA based hardware system of the PNN classifier realises about 94±2% testing accuracy. The FPGA system operates as fast as 50.777 MHz, corresponding to a clock period of 19.694 ns.

scholarly journals An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2272
Author(s):  
Safa Bouguezzi ◽  
Hana Ben Fredj ◽  
Tarek Belabed ◽  
Carlos Valderrama ◽  
Hassene Faiedh ◽  
...  
Keyword(s):  
Recognition Rate ◽  
Hardware Acceleration ◽  
Implementation Model ◽  
Gate Arrays ◽  
Proposed Model ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Computer Vision Applications ◽  
On Chip ◽  
Segmentation Image

Convolutional Neural Networks (CNN) continue to dominate research in the area of hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its effectiveness in a variety of computer vision applications such as object segmentation, image classification, face detection, and traffic signs recognition, among others. However, there are numerous constraints for deploying CNNs on FPGA, including limited on-chip memory, CNN size, and configuration parameters. This paper introduces Ad-MobileNet, an advanced CNN model inspired by the baseline MobileNet model. The proposed model uses an Ad-depth engine, which is an improved version of the depth-wise separable convolution unit. Moreover, we propose an FPGA-based implementation model that supports the Mish, TanhExp, and ReLU activation functions. The experimental results using the CIFAR-10 dataset show that our Ad-MobileNet has a classification accuracy of 88.76% while requiring little computational hardware resources. Compared to state-of-the-art methods, our proposed method has a fairly high recognition rate while using fewer computational hardware resources. Indeed, the proposed model helps to reduce hardware resources by more than 41% compared to that of the baseline model.

Fifty years of Electronic Hardware Implementations of First and Higher Order Neural Networks

2010 ◽  
pp. 269-285 ◽  
Author(s):  
David R. Selviah ◽  
Janti Shawash
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
High Speed ◽  
Higher Order ◽  
Low Latency ◽  
Real Time Control ◽  
Practical Applications ◽  
Field Programmable ◽  
On Chip ◽  
Electronic Hardware

This chapter celebrates 50 years of first and higher order neural network (HONN) implementations in terms of the physical layout and structure of electronic hardware, which offers high speed, low latency, compact, low cost, low power, mass produced systems. Low latency is essential for practical applications in real time control for which software implementations running on CPUs are too slow. The literature review chapter traces the chronological development of electronic neural networks (ENN) discussing selected papers in detail from analog electronic hardware, through probabilistic RAM, generalizing RAM, custom silicon Very Large Scale Integrated (VLSI) circuit, Neuromorphic chips, pulse stream interconnected neurons to Application Specific Integrated circuits (ASICs) and Zero Instruction Set Chips (ZISCs). Reconfigurable Field Programmable Gate Arrays (FPGAs) are given particular attention as the most recent generation incorporate Digital Signal Processing (DSP) units to provide full System on Chip (SoC) capability offering the possibility of real-time, on-line and on-chip learning.

A Primer for Telemetry Interfacing in Accordance with NASA Standards Using Low Cost FPGAs

2016 ◽  
Vol 05 (01) ◽  
pp. 1640002
Author(s):  
Jake McCoy ◽  
Ted Schultz ◽  
James Tutt ◽  
Thomas Rogers ◽  
Drew Miles ◽  
...  
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Photon Counting ◽  
Sounding Rocket ◽  
Photon Counting Detector ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Custom Hardware ◽  
Commercial Off The Shelf ◽  
Main Component

Photon counting detector systems on sounding rocket payloads often require interfacing asynchronous outputs with a synchronously clocked telemetry (TM) stream. Though this can be handled with an on-board computer, there are several low cost alternatives including custom hardware, microcontrollers and field-programmable gate arrays (FPGAs). This paper outlines how a TM interface (TMIF) for detectors on a sounding rocket with asynchronous parallel digital output can be implemented using low cost FPGAs and minimal custom hardware. Low power consumption and high speed FPGAs are available as commercial off-the-shelf (COTS) products and can be used to develop the main component of the TMIF. Then, only a small amount of additional hardware is required for signal buffering and level translating. This paper also discusses how this system can be tested with a simulated TM chain in the small laboratory setting using FPGAs and COTS specialized data acquisition products.

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