Eta Compute Inc. has begun shipping its multicore processor for embedded sensor applications. The ECM3532 is a Neural Sensor Processor (NSP) for unique multicore device features the company’s patented Continuous Voltage Frequency Scaling (CVFS) and delivers power consumption of microwatts for many sensing applications.
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.
“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, in a statement. “We are excited to see the first of many applications our customers are developing come to market later this year.”
The 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 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 a MCU and a DSP, both with CVFS, to optimize execution for the best efficiency, making it an ideal solution for IoT sensor nodes.
The processor is packaged in a 5 x 5 mm 81 ball BGA. It consumes as little as 100μW active power consumption in always-on applications. It incorporates a Arm Cortex-M3 processor with 256KB SRAM, 512KB Flash and a 16b Dual MAC DSP with 96KB dedicated SRAM for ML acceleration. The processor comes with a neural development software development kit with TensorFlow interface for seamless model integration.