In turn, this will allow drones to nearly instantaneously react to potentially dangerous situations,” says Ilja Ocket. 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. “Hence, a flagship use-case for our new chip includes the creation of a low-latency, low-power anti-collision system for drones. limited battery capacity) that need to react quickly to changes in their environment in order to appropriately react to approaching obstacles. The drone industry – even more than the automotive sector – works with constrained devices (e.g. Use-case: a smarter, low-power anti-collision system for drones (and cars) 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. Yet thanks to its generic architecture that features a completely new digital hardware design, it can also easily be reconfigured to process a variety of other sensory inputs like sonar, radar and lidar data. Imec’s novel chip was initially designed to support electrocardiogram (ECG) and speech processing in power-constrained devices. The technology we are introducing today is a major leap forward in the development of truly self-learning systems.” 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. As such, energy consumption can significantly be reduced. “SNNs operate very similarly to biological neural networks, in which neurons fire electrical pulses sparsely over time, and only when the sensory input changes. “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. 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. For one, they consume too much power to be integrated into increasingly constrained (sensor) devices. But ANNs come with their share of limitations. They are a key ingredient, for instance, of the radar-based anti-collision systems commonly used in the automotive industry. 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.Īrtificial neural networks (ANNs) have already proven their worth in a wide range of application domains. For example, micro-Doppler radar signatures can be classified using only 30 μW of power. Mimicking the way groups of biological neurons operate to recognize 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. LEUVEN (Belgium), ApImec, a world-leading research and innovation hub in nanoelectronics and digital technologies, today presents the world’s first chip that processes radar signals using a spiking recurrent neural network.
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