  AI-powered edge devices currently lack the ability to adapt their embedded
inference models to the ever-changing environment. To tackle this issue,
Continual Learning (CL) strategies aim at incrementally improving the decision
capabilities based on newly acquired data. In this work, after quantifying
memory and computational requirements of CL algorithms, we define a novel HW/SW
extreme-edge platform featuring a low power RISC-V octa-core cluster tailored
for on-demand incremental learning over locally sensed data. The presented
multi-core HW/SW architecture achieves a peak performance of 2.21 and 1.70
MAC/cycle, respectively, when running forward and backward steps of the
gradient descent. We report the trade-off between memory footprint, latency,
and accuracy for learning a new class with Latent Replay CL when targeting an
image classification task on the CORe50 dataset. For a CL setting that retrains
all the layers, taking 5h to learn a new class and achieving up to 77.3% of
precision, a more efficient solution retrains only part of the network,
reaching an accuracy of 72.5% with a memory requirement of 300 MB and a
computation latency of 1.5 hours. On the other side, retraining only the last
layer results in the fastest (867 ms) and less memory hungry (20 MB) solution
but scoring 58% on the CORe50 dataset. Thanks to the parallelism of the
low-power cluster engine, our HW/SW platform results 25x faster than typical
MCU device, on which CL is still impractical, and demonstrates an 11x gain in
terms of energy consumption with respect to mobile-class solutions.
