Programmable synaptic memory with spiking neural network in VLSI

Abstract

This work evaluates the performance of Neuromorphic architecture for accessing Static Random Access Memory (SRAM) in an asynchronous manner. Spike-Timing Dependent Plasticity (STDP) learning algorithm for updating the synaptic weights values in the SRAM module. Input, output, and synaptic weight values are transmitted to/from the chip using a common communication protocol based on the Address Event Representation (AER). Memory array an important block in digital system. Bassically SRAM is used for increasing speed of the processor. Which is mostly connected between the DRAM and microprocessor. SRAM serves as cache memory which stores the information and losses its data without power. We may design the SRAM using 4T, 6T, 8T....for storing one bit. Sense amplifier is used to perform read and write operation. Instead of using sense amplifier we use the neural network concept. Synapse is connected between the SRAM and neuron. The minute gap across which nerve impulses pass from one neurone to the next, at the end of a nerve fiber. Each neurone has an enlarged portion the cell body, containing the nucleus; from the body extend several process through which impulses enter from their branches. A longer process, the nerve fiber, extends outwards and carries impulse away from the cell body. This is normally unbranched except at the nerve ending. This consumes less power than the normal memory circuit.