Tiny spiking AI for the sensor-edge
Petrut BOGDAN, Neuromorphic Architect, Innatera
The brain relies on a powerful computing paradigm known as the spiking neural network (SNN) to realize its cognitive functions. SNNs encode sensory information as simple, precisely-timed voltage pulses – or spikes – and realize advanced cognitive functions by leveraging the fine-grained temporal relationships between sequences of spikes. This principle underpins the brain’s ability to memorize and robustly recognize complex patterns in noisy sensory data. Innatera applies the principles of SNNs toward overcoming the challenges of always-on sensing applications in power-limited and battery-
Innatera’s Spiking Neural Processor (SNP) is an analog-mixed signal processing platform that leverages SNNs for high-performance signal processing and pattern recognition in sensing applications.
The SNP implements a proprietary continuous-time analog processing architecture that utilizes highly parameterized silicon neurons and synapses to carry out analog-domain processing on sparse, spike-based representations of sensor data. The combination of massively parallel execution, radically low power dissipation of the analog-mixed signal computing elements, and the event-driven nature of SNNs together allows the SNP to realize complex signal processing and pattern recognition functions within a sub-milliwatt power envelope and sub-millisecond latencies. Applications for the SNP are developed using the PyTorch-compatible Talamo SDK which simplifies the development, optimization, and deployment of SNNs onto this innovative new hardware.
The unprecedented combination of ultra-low power, low latency, and compact models enables the SNP to realize complex spatio-temporal pattern recognition capabilities even in battery-powered sensing devices. An example of such an application is acoustic scene classification, where audio data streams are continuously processed to identify the type of ambient noise. The SNP’s capabilities are demonstrated in this application, yielding inference with state-of-the-art accuracy figures, power dissipation 100μW and latency 50ms. This highlights the promise of SNNs in addressing the challenges of sensing applications and underscores why the next generation of tiny ML at the sensor edge is neuromorphic.