TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs

TL;DR

This paper introduces TNN7, a suite of nine highly optimized macros for neuromorphic TNNs, significantly improving efficiency, modularity, and synthesis speed in TNN hardware implementations.

Contribution

TNN7 provides a set of custom macros built with a 7nm process, enhancing TNN design efficiency and scalability compared to previous microarchitecture frameworks.

Findings

TNN7 macros reduce power by 14% and delay by 16%.

TNN7 macros decrease area by 28% and energy-delay product by 45%.

TNN7 accelerates synthesis runtime by over 3 times.

Abstract

Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitecture framework for implementing TNNs and demonstrated competitive performance on vision and time-series applications. Building on these previous works, this work proposes TNN7, a suite of nine highly optimized custom macros developed using a predictive 7nm Process Design Kit (PDK), to enhance the efficiency, modularity and flexibility of the TNN design framework. TNN prototypes for two applications are used for evaluation of TNN7. An unsupervised time-series clustering TNN delivering competitive performance can be implemented within 40 uW power and 0.05 mm^2 area, while a 4-layer TNN that achieves an MNIST error rate of 1% consumes only 18 mW and 24.63 mm^2. On average, the proposed macros reduce power, delay, area, and energy-delay product by 14%, 16%, 28%, and 45%, respectively. Furthermore, employing TNN7 significantly reduces the synthesis runtime of TNN designs (by more than 3x), allowing for highly-scaled TNN implementations to be realized.