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GrAI Matter Exploits Sparse Video

October 29, 2019

Author: Linley Gwennap

Most security cameras have a boring life, staring at the same scenery day and night, hoping for the occasional car or person to pass by. It’s enough to put any watcher to sleep. So why design a chip to run the same neural network on every video frame, continuously burning power? Startup Grai Matter Labs has taken a different approach, creating a hardware architecture that can ignore unchanging information, allowing most of the neurons to sleep while activating only those that receive new data. The company introduced its Grai One accelerator at the recent Linley Fall Processor Conference and expects it to reach production in 1H20.

As the name suggests, Grai One is the first chip to implement the startup’s NeuronFlow architecture. The tiny device employs 196 cores that instantiate a total of 200,000 neurons. The design measures 20.2mm2 in TSMC’s 28nm HPC+ process. Each NeuronFlow core is only 0.06mm2, about 70% of which is SRAM. In this design, the cores operate at a maximum speed of 500MHz. Other than the cores and the NoC to connect them, Grai One implements only a set of general-purpose I/Os (GPIOs) to move data into and out of the chip. Future products will implement a more complete SoC design.

Although Grai Matter Labs refers to the architecture as neuromorphic, it isn’t restricted to the spiking neural networks (SNNs) that most other such chips perform; the architecture can also handle deep neural networks (DNNs) at up to 16-bit resolution. Each neuron is updated only when it receives a new input, allowing the chip to use as little as 35mW, although it requires 1.6W at full speed.

Subscribers can view the full article in the Microprocessor Report.

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