  Continual learning is a desirable feature in many modern machine learning
applications, which allows in-field adaptation and updating, ranging from
accommodating distribution shift, to fine-tuning, and to learning new tasks.
For applications with privacy and low latency requirements, the compute and
memory demands imposed by continual learning can be cost-prohibitive for
resource-constraint edge platforms. Reducing computational precision through
fully quantized training (FQT) simultaneously reduces memory footprint and
increases compute efficiency for both training and inference. However,
aggressive quantization especially integer FQT typically degrades model
accuracy to unacceptable levels. In this paper, we propose a technique that
leverages inexpensive Hadamard transforms to enable low-precision training with
only integer matrix multiplications. We further determine which tensors need
stochastic rounding and propose tiled matrix multiplication to enable low-bit
width accumulators. We demonstrate the effectiveness of our technique on
several human activity recognition datasets and CIFAR100 in a class incremental
learning setting. We achieve less than 0.5% and 3% accuracy degradation while
we quantize all matrix multiplications inputs down to 4-bits with 8-bit
accumulators.
