VLSI implementation of a binary neural network-two case studies

A comparison between a bit-level and a conventional VLSI implementation of a binary neural network is presented. This network is based on Correlation Matrix Memory (CMM) that stores relationships between pairs of binary vectors. The bit-level architecture consists of an n×m array of bit-level processors holding the storage and computation elements. The conventional CMM architecture consists of a RAM memory holding the CMM storage and an array of counters. Since we are interested in the VLSI implementation of such networks, hardware complexities and speeds of both bit-level and conventional architecture were compared by using VLSI tools. It is shown that a significant speedup is achieved by using the bit-level architecture since the speed of this last configuration is not limited by the memory addressing delay. Moreover, the bit-level architecture is very simple and reduces the bus/routing, making the architecture suitable for VLSI implementation. The main drawback of such an approach compared to the conventional one is the demand for a high number of adders for dealing with a large number of inputs