Visible to the public Biblio

Filters: Author is Wei, G. Y.  [Clear All Filters]
Conference Paper
Whatmough, P. N., Lee, S. K., Lee, H., Rama, S., Brooks, D., Wei, G. Y..  2017.  14.3 A 28nm SoC with a 1.2GHz 568nJ/prediction sparse deep-neural-network engine with \#x003E;0.1 timing error rate tolerance for IoT applications. 2017 IEEE International Solid-State Circuits Conference (ISSCC). :242–243.

This paper presents a 28nm SoC with a programmable FC-DNN accelerator design that demonstrates: (1) HW support to exploit data sparsity by eliding unnecessary computations (4× energy reduction); (2) improved algorithmic error tolerance using sign-magnitude number format for weights and datapath computation; (3) improved circuit-level timing violation tolerance in datapath logic via timeborrowing; (4) combined circuit and algorithmic resilience with Razor timing violation detection to reduce energy via VDD scaling or increase throughput via FCLK scaling; and (5) high classification accuracy (98.36% for MNIST test set) while tolerating aggregate timing violation rates \textbackslashtextgreater10-1. The accelerator achieves a minimum energy of 0.36μJ/pred at 667MHz, maximum throughput at 1.2GHz and 0.57μJ/pred, or a 10%-margined operating point at 1GHz and 0.58μJ/pred.