AI multicore processor for embedded sensor applications

1 min read

ETA Compute has begun shipping silicon for its ECM3532, its AI multicore processor for embedded sensor applications.

The multicore device features the company’s patented Continuous Voltage Frequency Scaling (CVFS) and delivers power consumption of microwatts for many sensing applications.

Eta Compute’s ECM3532 is a Neural Sensor Processor (NSP) for always-on image and sensor applications.

“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.

The ECM3532 family brings AI to edge devices and transforms sensor data into actionable information for voice, activity, gesture, sound, image, temperature, pressure, and bio-metrics applications, among others. The platform solves issues for the most important issues in edge computing: longer battery life, shorter response time, increased security and higher accuracy.

The company’s standalone AI platform includes a multicore processor, that includes flash memory, SRAM, I/O, peripherals and a machine learning software development platform. The patented CVFS, according to the company, substantially increases performance and efficiency for edge devices.

The self-timed CVFS architecture automatically and continuously adjusts internal clock rate and supply voltage to maximize energy efficiency for the given workload. The ECM3532 multicore NSP combines an MCU and a DSP, both with CVFS, to optimise execution for the best efficiency making it an ideal solution for IoT sensor nodes.

Key Features:

  • 5 x 5 mm 81 ball BGA
  • As low as 100μW active power consumption in always-on applications
  • Arm Cortex-M3 processor with 256KB SRAM, 512KB Flash
  • 16b Dual MAC DSP with 96KB dedicated SRAM for ML acceleration
  • Neural Development SDK with TensorFlow interface for seamless model integration into the ECM3532