A touchscreen McDonald’s kiosk running DOOM is one thing, training a Syntiant NDP200 neural decision processor to to play the game using very little power is another. This chip is designed to run neural networks and for vision processing at under 1mW, all the while performing highly accurate processing on-device, including multi-sensor fusion, voice command recognition, as well as tamper detection.
To train this processor to play DOOM, a lightweight version of the game was used, called VizDoom. Reinforcement learning was then employed to train a neural network consisting of several layers, with the first set being responsible for understanding what the network is seeing, and the last, taking action in response. A total of 600,000 parameters were required, which is significant because the NDP200 has just 640 kilobytes of onboard memory for neural-network parameters.
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The combination of Syntiant’s NDP200 and PixArt’s ultra-low-power image sensor offers the ability to develop highly accurate person and object detection systems that consume very little power with almost no latency. These features are extremely critical to the growing need for imaging sensor applications found in battery-operated IoT devices, such as video doorbells and security cameras,” said Sen Huang, CEO of PixArt Imaging, a leading provider of smart sensor SoC solutions.
