most citedAlways-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device

4 citations · 4 across the 3 of their papers we have counts for

collaborators

3 papers

eess.AS2021

PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition

Boris Bergsma, Minhao Yang, Milos Cernak

At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation ha…

eess.SP2020

A 6.3-Nanowatt-per-Channel 96-Channel Neural Spike Processor for a Movement-Intention-Decoding Brain-Computer-Interface Implant

Zhewei Jiang, Jiangyi Li, Pavan K. Chundi +6

This paper presents microwatt end-to-end neural signal processing hardware for deployment-stage real-time upper-limb movement intent decoding. This module features intercellular sp…

cs.NE2020★ 4 cited

Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device

Dewei Wang, Pavan Kumar Chundi, Sung Justin Kim +6

Always-on artificial intelligent (AI) functions such as keyword spotting (KWS) and visual wake-up tend to dominate total power consumption in ultra-low power devices. A key observa…