Hardware and Software Puzzle Pieces Fall Into Place for Binarized AI

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eetimes.com, Apr. 15, 2020 – 

Two British firms have partnered to accelerate the adoption of binarized neural networks (BNNs), a technology that will drastically reduce memory footprint for AI models in endpoint applications such as voice control and person detection.

XMOS (Bristol, UK) and Plumerai (London, UK) will work together to combine XMOS' crossover processor for voice-controlled IoT devices, Xcore.ai, with Plumerai's Larq software library for training BNNs.

The adoption of BNNs, which reduce parameters to 1-bit numbers, requires both new neural network models and special hardware that can support the 1-bit operations. Xcore.ai is one of the first non-ASIC parts with native support for the 1-bit vector arithmetic required for BNN inference.

"We're making deep learning tiny and computationally radically more efficient," Roeland Nusselder, CEO of Plumerai told EETimes. "For this, we have been developing software for the most efficient form of deep learning, which is binarized neural networks."

Plumerai, founded in 2017, employs 20 people spread between London, Amsterdam and Warsaw.

"[BNNs] need this hardware-software combination, this co-design. And that's always been a chicken and egg problem," Nusselder said. "There was no good hardware, that's why there was no good software and vice versa. But now with this partnership with XMOS, that's being solved."

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