Innatera's Neuromorphic AI Chip to Accelerate Spiking Neural Networks

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www.eetimes.eu, Sept. 27, 2021 – 

Innatera, a Dutch startup making neuromorphic AI accelerators for spiking neural networks, has produced its first chips, gauged their performance, and revealed details of their architecture.

The company also announced that Cadence and Synopsys co-founder Alberto Sangiovanni-Vincentelli has joined its board as chairman. The industry veteran is currently a professor at the University of California at Berkeley.

The Innatera chip is designed to accelerate spiking neural networks (SNNs), a type of neuromorphic AI algorithm based on brain biology. SNNs use the timing of spikes in an electrical signal to perform pattern-recognition tasks. They are completely different in structure from mainstream AI algorithms and thus require dedicated hardware for acceleration, but they typically offer significant power consumption and latency advantages for sensor edge applications.

Most other companies working on SNN algorithms and hardware (for example, Prophesee) are targeting images and video streams. Innatera has decided to focus on audio (sound and speech recognition), health (vital-signs monitoring), and radar (for consumer/IoT use cases, such as fall sensors that can monitor a person without invading privacy).

"These sensors have time-series data instead of images, which are very parallel," said Marco Jacobs, Innatera's vice president of marketing and business development, in an interview with EE Times. "Our array is especially good at processing time-series data ... it's a good technology fit. Also, from a market perspective, we see a lot of interesting applications in this area and not that many solutions that address it."

nother thing these three applications have in common is that, because processing is required in the sensor node, the power envelope is very tight. In Innatera's tests, each spike event (each neuron firing in response to input data) required less than a picojoule of energy – actually, less than 200 femtojoules in TSMC 28 nm, Innatera confirmed. That approaches the amount of energy used by biological neurons and synapses. A typical audio keyword-spotting application required fewer than 500 spike events per inference, resulting in "deep sub-milliwatt power dissipation," said Innatera's CEO, Sumeet Kumar. In this case, clusters of neurons firing together represent different phonemes in speech.

Processing architecture

Innatera's spiking neural processor uses a parallel array of spiking neurons and synapses to accelerate continuous-time SNNs with fine-grained temporal dynamics. The device is an analog/mixed-signal accelerator designed to leverage SNNs' ability to incorporate the notion of time in how the data is processed.

A key aspect of Innatera's compute fabrics is its programmability, which is important for two reasons.

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