IMEC's spiking neural network aids drone navigation

April 28, 2020 //By Peter Clarke
IMEC's spiking neural network aids drone navigation
The IMEC research institute has produced a spiking recurrent neural networking IC suitable for use with radar signals.

Spiking neural networks (SNNs) are closer analogues of the biological neural networks than common weighted-synapse neural networks. In SNNs data is moved as a series of electrical pulses over time but only when the sensory input changes. IMEC's chip consumes 1 percent of the power of traditional artificial NNs while providing a tenfold reduction in latency.

Radar for military and avionic applications has also had a traditional requirement for high-speed and high-performance digital signal processing and is increasingly being considered for road traffic and autonomous driving.

IMEC's chip has 336 neurons. Part of this neuron fabric is fully feed-forwaed connected, part has reconfigurable feed-back and feed-forward recurrent connections. It operates at a nominal 1.1V and was implemented in 40nm CMOS by TSMC. It can classify micro-Doppler radar signatures using only 30-microwatts of power although the architecture and algorithms can be applied to variety of one- and two-dimensional sensor data, including electrocardiogram, speech, sonar, radar and lidar streams. The first use case for IMEC's spiking neural network is an anti-collision radar system for drones.

Although artificial neural networks are used in automotive industry, IMEC points out that in power-constrained environments such as battery-powered drones the power consumption is too burdensome. In addition the time taken to move data from sensor and through the AI inference algorithm is too long.

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