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Always-On Sub-Microwatt Spiking Neural Network Based on Spike-Driven Clock- and Power-Gating for an Ultra-Low-Power

Pavan Kumar Chundi¹, Dewei Wang¹, Sung Justin Kim¹

¹Department of Electrical Engineering, Columbia University, New York City, NY, United States.

Frontiers in Neuroscience

|August 6, 2021

Summary

This study introduces a novel spiking neural network (SNN) classifier for ultra-low-power Internet-of-Things (IoT) devices, enabling always-on artificial intelligence (AI) functions with minimal power consumption.

Keywords:

always-on device clock and power gating event-driven architecture neuromorphic hardware spiking neural network

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Area of Science:

Artificial Intelligence
Computer Engineering
Low-Power Electronics

Background:

Always-on hardware in Internet-of-Things (IoT) devices significantly impacts power efficiency.
Minimizing power dissipation in always-on systems is crucial for device longevity and performance.
Sparse temporal input signals present an opportunity for power-efficient computation.

Purpose of the Study:

To develop a novel spiking neural network (SNN) classifier architecture.
To enable always-on artificial intelligence (AI) functions in ultra-low-power IoT devices.
To leverage the event-driven nature of SNNs for significant power reduction.

Main Methods:

Designed an SNN classifier architecture utilizing event-driven principles.
Implemented fine-grained clock generation, gating, and power gating techniques.
Fabricated a prototype in 65 nm CMOS technology.

Main Results:

Achieved ultra-low static power dissipation (75 nW at no activity, <300 nW at 100% activity) at 0.52 V.
Demonstrated competitive inference accuracy for keyword spotting (KWS) and other classification tasks.
Reduced power consumption by over three orders of magnitude compared to prior SNN hardware and 2.3X compared to state-of-the-art KWS hardware.

Conclusions:

The proposed SNN classifier architecture offers a highly power-efficient solution for always-on AI in IoT.
Event-driven design and fine-grained power management are key to achieving substantial power savings.
This approach enables advanced AI capabilities in resource-constrained IoT devices without compromising energy efficiency.