Yuji Murakami - Master にそのファイルは見つかりません

NASH: Neuro-inspired ArchitectureS in Hardware Project

Design of Neural Network Architecture on Reconfigurable Hardware for Traffic Light Detection in Autonomous Driving Vehicle

On-going Paper†

:Thesis_Japanes.pdf, Date: Janury 15, 2018
:Thesis_Japanes_2.pdf, Date: Janury 19, 2018

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Research Schedule - Please update it always according to your progress†

please insert your reseach schedule here. Due date: July 7, 2017

Step 1. Hardware implementation not spiking on FPGA
Step 3. Spiking back propagation function and simulation
Step 4. modify for NoC

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NASH seminars †

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Background and Motivation†

The biological brain implements massively parallel computations using a complex architecture that is different from current Von Neuman machine. Our brain is a low-power, fault-tolerant, and high-performance machine! It consumes only about 20W and brain circuits continue to operate as the organism needs even when the circuit (neuron, neuroglia, etc.) is perturbed or died. Conventional neural networks encode information with static input coding, eg. encoding a pattern as 0011 (binary bits) for 4 input neurons, and another pattern 0010. While in SNN, besides the pattern code, the time-related factors, eg. spiking rate, spiking rank and spiking intervals, can be used to present the information. This greatly increases the information processing capacity of a neural network. SNN only process information when spikes occur. As a result, SNNs consume almost no energy when no spikes occur [www]. A biological SNN (e.g. mammalian brains) uses a spike rate of only several KHz to finish a complex task (e.g. vision pattern recognition) with each spike consumes an energy of in fJ per synapse. Spiking neural network (SNN) simulations are a flexible and powerful method for investigating the behavior of neuronal systems. However, simulation of the spiking neural networks in software is slow. An alternative approach is a hardware implementation of such system, which provides the possibility to generate independent spikes accurately and simultaneously output spikes in real time. Also, spiking neural network can take full advantage of hardware inherent parallelism. SNN and ANN are widely used in signal processing, speech synthesis, pattern recognition, and so forth.

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