By Richard Quinnell, editor-in-chief

There
is a groundswell of activity in the microcontroller world to bring machine-learning
artificial intelligence (AI) to the network’s edge. But the computational
requirements of running algorithms such as convolutional neural networks (CNNs)
can be fairly demanding of power. Fabless semiconductor vendor GreenWaves
Technologies has addressed that issue with its release of a new processor that
applies AI to sensor readings to help distill data to reduce traffic.

GreenWaves
announced at Embedded World this week its GAP8 application processor, an
eight-core computational cluster that has been optimized to handle video- and
audio-processing algorithms as well as CNN. The processor also has a core
dedicated to communications, control, and sensor data pre-analysis. Target
applications for the device include object and face detection, vibration
analysis, and keyword spotting in audio data streams. Based on the open RISC-V
architecture
, the GAP8 is part of Europe’s parallel
ultra-low-power initiative
(PULP), designed to increase processing
performance at the edge in battery-powered IoT applications such as sensing.

The
advantage of having AI capabilities at the edge, GreenWaves vice president for
business development Martin Croome told EP in an interview, is that it can
dramatically reduce the amount of data flowing between the device and cloud
services. The AI allows the edge device to classify the data that it collects
instead and send only the classification results rather than the raw data. In a
people-counting application, for instance, giving the IoT camera the ability to
distinguish people from other objects in field of view can eliminate the need
for sending video information to the cloud. Instead, the device simply needs to
send the counts that it makes, reducing data rates from kilobytes per second to
a few bytes per day. Similarly, a simple voice-control application needing only
a handful of command words can operate almost entirely within the processing
capability of edge devices such as the GAP8.

Gapduino

The Gapuino development board will
provide an opportunity to quickly begin working with AI at the edge using the GAP8
processor. Image source: GreenWaves Technology.

The
processor’s architecture utilizes a shared instruction cache (SIMD) that feeds
nine identical processor cores. One core serves as the fabric controller and
activates the eight other cores to provide parallel processing as needed. The
controller is able to coordinate the resulting operations so that each core can
run a task independently until completion, then have the controller assemble
the results for use in the next instruction. Croome estimates that the processing
achieved is nearly 20 times better than traditional DSPs for AI functions.

Because
the GAP8 and its supporting software and development tools are built on open
platforms, the cost of this performance can be quite low. There are no
royalties or licensing fees involved, and the company’s development investment
only needed to be spent where they could add value rather than building the
foundation, so the company expects to be offering the chip for $5 in 100K
quantities. As a result, the company estimates, something like machine vision
and voice control of consumer robots could be done with hardware costing less
than $15.

The processor’s SDK has been available since
December, and GreenWaves will be rolling out a development board — the Gapuino — in April, allowing developers to quickly get
started on product development. The SDK supports multiple real-time operating
systems (including mBed) and includes an auto-tiler to help parallelize
application code and data streaming. There is also a code generator for common
AI algorithms such as CNN and software to bridge from TensorFlow AI
descriptions to Gap. The base development board can be pre-ordered for €100, and there are both sensor interface and
image plug-in expansion boards available.