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Wednesday, February 19, 2020

Energy efficient silicon ships for edge AI

By Nick Flaherty www.flaherty.co.uk

Eta Compute has shipped the first production version of its ECM3532 embedded AI processor.

The multicore chip uses a patented technology called Continuous Voltage Frequency Scaling (CVFS) for power consumption of microwatts for many sensing applications.

The Neural Sensor Processor (NSP) for local machine learning in always-on image and sensor applications at the edge of the Internet of Things (IoT). The self-timed CVFS architecture automatically and continuously adjusts internal clock rate and supply voltage to maximize energy efficiency for the given workload, typically 100μW.

The chip combines an ARM Cortex-M3 processor with 256KB SRAM and 512KB Flash as well as a 16b Dual MAC DSP with 96KB dedicated SRAM, both with CVFS, with flash memory, SRAM, I/O, peripherals and a machine learning software development platform.  A Neural Development SDK with TensorFlow interface provides the ML model integration.

“Our Neural Sensor Platform is a complete software and hardware platform that delivers more processing at the lowest power profiles in the industry. This essentially eliminates battery capacity as a barrier to thousands of IoT consumer and industrial applications,” said Ted Tewksbury, CEO of Eta Compute. “We are excited to see the first of many applications our customers are developing come to market later this year.”

“We believe that power consumption, latency and data generation combined with RF transmission are all factors limiting many sensing applications," said Jim Feldhan, president and founder at Semico Research. "It’s great seeing Eta Compute’s platform coming into the market. Their technology is orders of magnitude more power-efficient than any other technology I have seen to date and it will certainly make AI at the edge a reality.”
“It’s exciting to see innovative products for low power machine learning being launched at tinyML where experts from the industry, academia, start-ups and government labs share the innovations to drive the whole ecosystem forward,” said Pete Warden, Google Researcher and General Co-chair of the tinyML organization.

“We are amazed by the ECM3532 and its efficiency for machine learning in sensing applications,” said Zach Shelby, CEO of Edge Impulse. “It is an ideal fit for our TinyML lifecycle solution that transforms developers’ abilities to deploy ML for embedded devices by gathering data, building a model that combines signal processing, neural networks and anomaly detection to understand the real world.”

“Himax Imaging HM01B0 and new HM0360 are among the industry’s lowest power image sensors with autonomous operation modes and advanced features to reduce power, latency and system overhead. Our image sensors can operate in sub-mW range and when paired with the low power multi-core processors such as Eta Compute’s ECM3532, developers can quickly deploy edge devices that perform image inference under 1mW,” said Amit Mittra, CTO of Himax Imaging.

The ECM3532 is packaged in a 5 x 5 mm 81 ball BGA.

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