By mimicking the way groups of biological neurons operate to recognise temporal patterns, imec’s chip consumes 100 times less power than traditional implementations while featuring a tenfold reduction in latency – enabling almost instantaneous decision-making. For example, micro-Doppler radar signatures can be classified using only 30 mW of power.
While the chip’s architecture and algorithms can be tuned to process a variety of sensor data (including electrocardiogram, speech, sonar, radar and lidar streams), its first use-case will encompass the creation of a low-power, highly intelligent anti-collision radar system for drones that can react much more effectively to approaching objects.
Artificial neural networks (ANNs) have already proved to be useful across a wide range of application domains and are, for example, being used in radar-based anti-collision systems commonly used in the automotive industry. However, ANNs come with their share of limitations - they consume too much power to be integrated into increasingly constrained (sensor) devices. In addition, ANNs’ underlying
architecture and data formatting requires data to undertake a time-consuming journey from the sensor device to the AI inference algorithm before a decision can be made. Hence the decision to use spiking neural networks (SNNs).
“This is the world’s first chip that processes radar signals using a recurrent spiking neural network,” said Ilja Ocket, program manager of neuromorphic sensing at imec. “SNNs operate very similarly to
biological neural networks, in which neurons fire electrical pulses sparsely over time, and only when the sensory input changes. As such, energy consumption can significantly be reduced. What’s more, the spiking neurons on our chip can be connected recurrently – turning the SNN into a dynamic system that learns and remembers temporal patterns. The technology is a major leap forward in the development of truly self-learning systems.”
The chip was initially designed to support electrocardiogram (ECG) and speech processing in power-constrained devices. Due to its generic architecture that features a completely new digital hardware
“A flagship use-case for our new chip includes the creation of a low-latency, low-power anti-collision system for drones. Doing its processing close to the radar sensor, our chip should enable the radar sensing system to distinguish much more quickly – and accurately – between approaching objects. In turn, this will allow drones to nearly instantaneously react to potentially dangerous situations,” explained Ocket. “One scenario we are currently exploring features autonomous drones that depend on their on-board camera and radar sensor systems for in-warehouse navigation, keeping a safe distance from walls and shelves while performing complex tasks. This technology could be used in plenty of other use-cases as well – from robotics scenarios to the deployment of automatic guided vehicles (AGVs) and even health monitoring.”
IoT cognitive sensing at imec, concluded.
