A Software-Circuit-Device Co-Optimization Framework for Neuromorphic Inference Circuits

Abstract

Neuromorphic circuits, which usually use analog computation for vector-matrix multiplication (VMM) in neural networks (NN), are promising machine learning accelerators with much lower latency and power consumption than digital ones. Analog computation is expected to have a more efficient design space than digital computation since the signals are not digitized. Therefore, it is very suitable for Internet-of-Thing (IoT) applications that require ultra-low power consumption at a low cost. For IoT applications, sometimes it is also desirable to eliminate the digital circuits (such as adders, registers, shifters, multiplexers, and Analog-to-Digital Converters) between the VMM arrays to further reduce the power consumption. However, the optimization of a purely analog circuit is more difficult and requires full SPICE circuit simulations. In this paper, we present a software-circuit-device co-optimization framework using a python wrapper for automatic full circuit SPICE simulation and analysis for neuromorphic circuits. This framework allows users to experiment with how the NN design (software) affects the performance of the hardware neuromorphic circuits. It takes Verilog-A or SPICE models from calibrations or PDK in various technologies and emerging memories (such as ReRAM) without further calibration (unlike using behavior models). We show that the simulation time is reasonable even with hundreds of thousands of synapses under limited computation resources. Using ReRAM and a 45nm generic technology as an example, the effects of feedback network and OpAmp design, software ML architecture, and input data accuracy on the inference accuracy are studied.

Keywords

• Device-technology co-optimization (DTCO)
• emerging memory
• neural network
• neuromorphic computation
• ReRAM
• SPICE simulation