An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems

Abstract

This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in terms of operating frequency, power and noise. The circuitlevel implementation of the MS-N in 65 nm CMOS technology exhibits 2-3 orders of magnitude better energy-efficiency over Dig-N for neuromorphic computing applications -especially at low frequencies due to the high leakage currents from many transistors in Dig-N. The inherent error-resiliency of CNN is exploited to handle the thermal and flicker noise of MS-N. A system-level analysis using a cohesive circuit-algorithmic framework on MNIST and CIFAR-10 datasets demonstrate an increase of 3% in worst-case classification error for MNIST when the integrated noise power in the bandwidth is ~ 1 µV 2 .

Effects of RF Signal Eventization Encoding on Device Classification Performance

“…This paper summarizes first-step research activity aimed at realizing an envisioned "event radio" capability that mimics neuromorphic event-based camera processing. The energy efficiency of neuromorphic processing is orders of magnitude higher than traditional von Neumann-based processing architectures (10× to 1000× [17][18][19]) and realized through synergistic design of brain-inspired software [47] and hardware [22] computing elements. The development and availability of event-based hardware devices supporting Radio Frequency (RF) applications are severely lagging when compared with activity in the event-based camera arena.…”

“…Advances in neuromorphic computing [15] and the use of Spiking Neural Network (SNN) classification architectures [16] have enabled near-equivalent classification performance as obtained with traditional von Neumann-based artificial neural network architectures at a fraction of the energy consumption. While neuromorphic computing has shown energy reduction approaching 1000× in selected applications [17][18][19], its impact on radio-frequency (RF) applications remains largely uninvestigated.…”

Exploiting Inherent Error Resiliency of Deep Neural Networks to Achieve Extreme Energy Efficiency Through Mixed-Signal Neurons

“…For the 8-bit design, the MS-N is ~870X better than Dig-N in terms of energy-efficiency at near-threshold point (~10 MHz), which is a further 4X improvement over the performance of the MS-N presented in section II. C and in [24].…”