Electrical engineering

Neuromorphic engineering

Resistive random-access memory

Computer architecture

Engineering

Voltage

Parallel computing

Artificial intelligence

Reconfigurability

Computer science

Probabilistic logic

Static random-access memory

Computer hardware

Dataflow

Artificial neural network

Telecommunications

Many powerful neural network (NN) models such as probabilistic graphical models (PGMs) and recurrent neural networks (RNNs) require flexibility in dataflow and weight access patterns as shown in Fig. 33.1.1 Typically, Compute-In-Memory (CIM) designs do not implement such dataflows or do so by replicating circuits at the memory periphery such as ADCs/neurons along both the rows and columns of the memory array, leading to an overhead in operation. This paper describes a CIM architecture implemented in a 130nm CMOS/RRAM process, that delivers the highest reported computational energy-efficiency of 74 tera-multiply-accumulates per second per watt (TMACS/W) for RRAM-based CIM architectures while simultaneously offering dataflow reconfigurability to address the limitations of previous designs. This is made possible through two key features: 1) a runtime reconfigurable dataflow with in-situ access to RRAM array and its transpose for efficient access to NN weights and 2) a voltage sensing stochastic integrate-and-fire analog neuron (I&F) that is reused for correlated double sampling (CDS), stochastic voltage integration, and threshold comparison.

33.1 A 74 TMACS/W CMOS-RRAM Neurosynaptic Core with Dynamically Reconfigurable Dataflow and In-situ Transposable Weights for Probabilistic Graphical Models