€5 octacore MCU covers analytics, fusion at the edge

March 19, 2018 // By Peter Clarke
Fabless chip company GreenWaves Technologies SAS (Greenoble, France) has launched its GAP8 RISC-V-based multicore processor.

The company originally announced the design back in November 2016 (see IoT processor beats Cortex-M, claims startup) but then had to wait until December 2017 before it could tape out. Martin Croome, vice president of business development, said it took longer than expected to raise funds but the time was also used to make some improvements to the design

GreenWaves eventually announced the raising of €3.1 million of funding (about $3.8 million) in August 2017.

The GAP8 is based on the RISC-V opens-source hardware PULP core developed at the Universities of Bologna and ETF Zurich (see Swiss open-source processor core ready for IoT ). It includes eight such cores plus a ninth as a controller for a microcontroller section. There is also a hardware convolution engine (HWCE) to accelerate neural network operations.

The chip has been designed in the 55LP 55nm CMOS process from foundry TSMC. This operates at 1.2V nominal in the core and has an I/O that operatest at 1.8 to 3.3V. The chip is intended to operate at clock frequencies up to 175MHz providing significant performance from its 8-core cluster. The design it makes use of extensions to the RISC-V instruction set to help with signal processing and boosting of convolutional neural networking performance.

The GAP8 is decribed as an IoT application processor and its intended to operate at the edge of the network autonomously to capture, analyze, classify and act on the fusion of data sources such as images, sounds or vibrations. GAP8 is optimized to execute image and audio algorithms including convolutional neural network (CNN) inference, with extreme energy efficiency. A separate core, within an independent voltage and frequency domain, takes care of communication, control and information pre-analysis. This allows industrial and consumer product manufacturers to integrate artificial intelligence and advanced classification into new classes of wireless sensing devices for IoT applications including image recognition, counting people and objects, machine health monitoring, home security, speech recognition, consumer robotics, wearables and smart toys.

Next: Production volumes and development board


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