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

• URL: http://arxiv.org/abs/2205.07410v1
• Date: Mon, 16 May 2022 01:03:41 GMT
• Title: TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs
• Authors: Harideep Nair, Prabhu Vellaisamy, Santha Bhasuthkar, and John Paul Shen
• Abstract summary: Temporal Neural Networks (TNNs) exhibit energy-efficient online sensory processing capabilities. This work proposes TNN7, a suite of nine highly optimized custom macros developed using a predictive 7nm Process Design Kit (PDK) An unsupervised time-series clustering TNN delivering competitive performance can be implemented within 40 uW power and 0.05 mm2 area. A 4-layer TNN that achieves an MNIST error rate of 1% consumes only 18 mW and 24.63 mm2.
• Score: 2.9068923524970227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitecture design framework for implementing TNNs and demonstrated competitive performance on vision and time-series applications. Building on them, 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.

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