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BrainChip Aims to Spike Neural Nets

May 22, 2018

Author: Mike Demler

BrainChip specializes in neuromorphic processing that employs spiking neural networks (SNNs), which emulate how the brain handles sensory input by transmitting electrical impulses between neurons in the cerebral cortex.

Biological neurons respond to incoming spikes on the basis of their spatiotemporal relationships. Transmitting neurons send spikes through their axons to the synapses of numerous receiving neurons. The receiving neuron’s potential energy changes according to the weight of each synaptic connection, meaning some neural connections are more significant than others. The timing of each spike is also important, since the neuron’s accumulated potential energy decays over time.

The synaptic-weighting effect can be positive (excitatory) or negative (inhibitory). When the sum of the potentials induced by the incoming spikes crosses a threshold, it causes the receiving neuron to output its own spike, which is effectively an encoding of the information the brain has learned about the relationship of the inputs.

A CNN recognizes objects by operating directly on pixel data, progressively extracting and identifying higher-level features by convolving small windows in the image. But an SNN, to mimic the brain’s behavior, first converts the image into an array of spikes. It emulates the visual cortex, converting colors, contrast changes, and the line segments constituting an object’s shape into a spike train.

The company has developed techniques for converting sensor data to spikes and for modeling SNN behavior in its hardware and software. Its first hardware product is an FPGA implementation called the BrainChip Accelerator, but it’s developing an SoC, called Akida, that it expects to tape out in 4Q18.

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