Imec claims to have built the world’s first spiking neural network (SNN) based chip for radar signal processing, enabling the creation of applications such as smart, low-power anti-collision radar systems for drones that identify approaching objects in a matter of milliseconds
Mimicking the way groups of biological neurons operate to recognize temporal patterns, imec said its 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 easily 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 been established in a wide range of application domains. They are a key ingredient, for instance, of the radar-based anti-collision systems commonly used in the automotive industry. But ANNs come with limitations. For one, they consume too much power to be integrated into increasingly constrained (sensor) devices. Additionally, 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. That’s where spiking neural networks (SNNs) can help.
“Today, we present the world’s first chip that processes radar signals using a recurrent spiking neural network,” says 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 we are introducing today is a major leap forward in the development of truly self-learning systems.”
Imec said its chip was initially designed to support electrocardiogram (ECG) and speech processing in power-constrained devices. Its generic architecture based on a completely new digital hardware design means it can also easily be reconfigured to process a variety of other sensory inputs like sonar, radar and lidar data. Contrary to analog SNN implementations, imec’s event-driven digital design makes the chip behave exactly and repeatedly as predicted by the neural network simulation tools.
Smart low-power anti-collision system for drones (and cars)
A key application for the new imec chip is a low-latency, low-power anti-collision system for drones. The drone industry – even more than the automotive sector – works with constrained devices (e.g. limited battery capacity) that need to react quickly to changes in their environment in order to appropriately react to approaching obstacles. Doing its processing close to the radar sensor, the chip should enable the radar sensing system to distinguish much more quickly – and accurately – between approaching objects. In turn, imec said this will allow drones to nearly instantaneously react to potentially dangerous situations.
Ocket commented, “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.” The chip meets a demand for low-power neural networks that learn from data and enable personalized AI. To create the chip, imec had experts from various disciplines within the research institute work together – from the development of training algorithms and spiking neural network architectures that take neuroscience as a basis, to biomedical and radar signal processing and ultra-low power digital chip design.
>> This article was originally published on our sister site, EE Times Europe.