Fully Binary Neural Network Model and Optimized Hardware Architectures for Associative Memories

Abstract

Brain processes information through a complex hierarchical associative memory organization that is distributed across a complex neural network. The GBNN associative memory model has recently been proposed as a new class of recurrent clustered neural network that presents higher efficiency than the classical models. In this article, we propose computational simplifications and architectural optimizations of the original GBNN. This work leads to significant complexity and area reduction without affecting neither memorizing nor retrieving performance. The obtained results open new perspectives in the design of neuromorphic hardware to support large-scale general-purpose neural algorithms.

Clone-Based Encoded Neural Networks to Design Efficient Associative Memories

“…More details can be found in [8] for the model and in [25] for the fully-binary model. The ENN has a storage capacity of the order N 2 /(log N) 2 messages for N neurons, while the standard Hopfield model has a capacity of N/(2 log N) when the messages are independent and identically distributed (i.i.d).…”