BrainChip Holdings Ltd. Claims that it’s the first company to deliver a spiking neural network (SNN) architecture to market. The company will begin sampling its new neuromorphic system-on-a-chip (SoC) in the third quarter of 2019, preceded by an FPGA-based development board for designers anticipating the introduction. The Akida Development Environment is available now for early-access customers.
BrainChip’s forthcoming Akida Neuromorphic SoC (NSoC) will implement a newer approach to machine learning called spiking neural networks. SNNs are said to learn much more rapidly than other machine-learning systems in nearly real time, using smaller data sets, all while relying on significantly less processing, which translates into lower power consumption.
This combination of traits will make SNNs particularly suitable for applications at the network edge, argues BrainChip. But first, the company needs to get the chips in the market and in use.
There are SNN chips available today. Prominent among them are Intel’s Loihi and the IBM’s TrueNorth, the latter developed under a DARPA contract.
Bob Beachler, BrainChip’s senior vice president of marketing, explained to Electronic Products that Loihi and TrueNorth are essentially experimental or research devices, whereas BrainChip’s Akida Neuromorphic SoC will be a commercial product for the commercial market. Given that most other companies appear to be pursuing a different network architecture known as convolutional neural networks (CNNs), BrainChip appears to have a good shot at being first with a commercial SNN-based machine-learning chip.
The Akida chip will have a neuron fabric with 1.2 million neurons and 10 billion synapses, on-chip processing (for system management and training/inference control), memory interfaces (for flash or LP/DDR4), a set of data interfaces for co-processor applications, and a chip-to-chip interface so that multiple Akida SoCs can be ganged. An on-board sensor interface currently supports five different sensor types, including pixel-based imaging and dynamic vision sensors; it is possible to create support for other types of sensors.