Eta Compute Announces Production Silicon of the Energy-Efficient Edge AI Processor
February 25, 2020
Eta Compute?s ECM3532 family brings AI to edge devices for sensor data into actionable information for voice, activity, gesture, sound, image, temperature, pressure, and bio-metrics applications.
Eta Compute announced the first shipment of production silicon for its ECM3532, the AI multicore processor for embedded sensor applications. This multicore device features the company’s patented Continuous Voltage Frequency Scaling (CVFS) and delivers power consumption of microwatts for sensing applications.
The ECM3532 is a Neural Sensor Processor (NSP) for always-on image and sensor applications.
Eta Compute’s ECM3532 family brings AI to edge devices for sensor data into actionable information for voice, activity, gesture, sound, image, temperature, pressure, and bio-metrics applications. The platform solves issues in edge computing including 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 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 optimize execution for the efficiency, making it an ideal solution for IoT sensor nodes.
- 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
The product was on display at the 2020 tinyML Summit, Feb. 12-13, at Samsung Electronics in San Jose, California. Eta Compute is a Gold Sponsor of tinyML and demonstrated the ECM3532 for image recognition and other edge sensing applications. The objective of the entire tinyML community is to enable ultra-low power machine learning at the network edge.
For more information visit EtaCompute.com.