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. 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."