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. Therefore, shaping the magnitude frequency response of MTI filters demands the flexibilities offered by neural networks.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. There are several other conflicting requirements for the optimum MTI design where the algorithmic procedures may not be as efficient. The MTI implementation with neural networks reduces the number of required independent pulses for Doppler shift extraction in the presence of clutter. The nonlinear processing capability of a neural network is utilized to efficiently combine the Doppler processing and integration performed by conventional MTI and its coprocessor (i.e., integrator).
Furthermore, it is easier to shape the frequency response of the neural network-based MTI (NN-MTI) as desired without needing the complex process of pole placement, which is traditionally required in both digital and analog filter design procedures. This paper provides some evidence that Doppler shifts can easily be extracted with neural networks even in situations where only a limited number of noisy pulses are available for processing. On the other hand, biological systems (e.g., insects, birds) have capabilities far beyond those of the conventional MTI processors. Although techniques of radar signal processing, including the moving target indicator (MTI), have been vastly improved by the availability of digital computers in recent years, these methods are generally based on complex mathematical procedures which make the engineering and design of radar receivers rather costly and vulnerable to electronic faults.