Neuromorphic computing runs on asynchronous Cortex-M3

March 14, 2018 // By Peter Clarke
Neuromorphic computing runs on asynchronous Cortex-M3
Well-connected IP licensing startup Eta Compute Inc. (Westlake Village, Calif.) has launched a neuromorphic computing platform for low-power machine learning and artificial intelligence at the edge.

The 55nm node based platform includes a Cortex-M3 processor from ARM, a Coolflux DSP from NXP Semiconductors, 12bit successive approximation register (SAR) analog-to-digital converter, power management and support for analog blocks such as power on reset, brown-out detector, oscillators, temp sensor, crystal oscillator, and RC oscillators.

Eta Compute reckons to have a couple of advantages for its microcontroller platform. One is the ability to operate at voltages down to around 0.2V the other is its spiking neural network software.

The company was founded in 2015 on a mission to get to deep sub-threshold voltages – and therefore extreme low power – and to do so it turned to asynchronous logic. Eta has applied its delay-insensitive asynchronous logic (DIAL) technology to the Cortex-M3 and the Coolflux DSP to provide an extremely low-power processor that can consume almost nothing when waiting for an event but can scale up to conventiona performance at about 100MHz clock frequency when required. The platform has been proven in silicon in TSMC's 55nm ULP process, the company said.

On its own that should give Eta Compute a best in class conventional microcontroller. But the second advantage is the development of spiking neural network algorithms that run on a soft artificial intelligence engine hosted on the Cortex-M3. This engagement with artificial intelligence has come about largely with the recruitment of Nara Srinivasa as chief technology officer in 2017.

"Our patented event driven processor architecture (DIAL) is combined with our fully customizable neuromorphic algorithms,” said Srinivasa, in a statement. "These will be the foundation of a diverse and wide-ranging set of applications that deliver machine intelligence to the network edge." Prior to joining Eta Compute Srinivasa worked at Intel Labs including research into spiking neural network modelling and performance.

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