A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas
This work addresses the efficiency gap for neuromorphic processors in adaptive edge computing and vision, offering a hardware-efficient solution with incremental improvements in on-chip learning compatibility.
The authors tackled the challenge of making neuromorphic processors efficient for real-world vision applications by proposing SPOON, a 28-nm event-driven CNN with online learning, achieving 95.3% accuracy on MNIST with on-chip training and an energy per classification of 313nJ at 0.6V.
In an attempt to follow biological information representation and organization principles, the field of neuromorphic engineering is usually approached bottom-up, from the biophysical models to large-scale integration in silico. While ideal as experimentation platforms for cognitive computing and neuroscience, bottom-up neuromorphic processors have yet to demonstrate an efficiency advantage compared to specialized neural network accelerators for real-world problems. Top-down approaches aim at answering this difficulty by (i) starting from the applicative problem and (ii) investigating how to make the associated algorithms hardware-efficient and biologically-plausible. In order to leverage the data sparsity of spike-based neuromorphic retinas for adaptive edge computing and vision applications, we follow a top-down approach and propose SPOON, a 28-nm event-driven CNN (eCNN). It embeds online learning with only 16.8-% power and 11.8-% area overheads with the biologically-plausible direct random target projection (DRTP) algorithm. With an energy per classification of 313nJ at 0.6V and a 0.32-mm$^2$ area for accuracies of 95.3% (on-chip training) and 97.5% (off-chip training) on MNIST, we demonstrate that SPOON reaches the efficiency of conventional machine learning accelerators while embedding on-chip learning and being compatible with event-based sensors, a point that we further emphasize with N-MNIST benchmarking.