Extending memory capacity of neural associative memory based on recursive synaptic bit reuse

Neural associative memory (AM) is one of the critical building blocks for cognitive workloads such as classification and recognition. It learns and retrieves memories as humans brain does, i.e., changing the strengths of plastic synapses (weights) based on inputs and retrieving information by information itself. One of the key challenges in designing AM is to extend memory capacity (i.e., memories that a neural AM can learn) while minimizing power and hardware overhead. However, prior arts show that memory capacity scales slowly, often logarithmically or in squire root with the total bits of synaptic weights. This makes it prohibitive in hardware and power to achieve large memory capacity for practical applications. In this paper, we propose a synaptic model called recursive synaptic bit reuse, which enables near-linear scaling of memory capacity with total synaptic bits. Also, our model can handle input data that are correlated, more robustly than the conventional model. We experiment our proposed model in Hopfield Neural Networks (HNN) which contains the total synaptic bits of 5kB to 327kB and find that our model can increase the memory capacity as large as 30X over conventional models. We also study hardware cost via VLSI implementation of HNNs in a 65nm CMOS, confirming that our proposed model can achieve up to 10X area savings at the same capacity over conventional synaptic model.