  The Continuous Learning (CL) paradigm consists of continuously evolving the
parameters of the Deep Neural Network (DNN) model to progressively learn to
perform new tasks without reducing the performance on previous tasks, i.e.,
avoiding the so-called catastrophic forgetting. However, the DNN parameter
update in CL-based autonomous systems is extremely resource-hungry. The
existing DNN accelerators cannot be directly employed in CL because they only
support the execution of the forward propagation. Only a few prior
architectures execute the backpropagation and weight update, but they lack the
control and management for CL. Towards this, we design a hardware architecture,
TinyCL, to perform CL on resource-constrained autonomous systems. It consists
of a processing unit that executes both forward and backward propagation, and a
control unit that manages memory-based CL workload. To minimize the memory
accesses, the sliding window of the convolutional layer moves in a snake-like
fashion. Moreover, the Multiply-and-Accumulate units can be reconfigured at
runtime to execute different operations. As per our knowledge, our proposed
TinyCL represents the first hardware accelerator that executes CL on autonomous
systems. We synthesize the complete TinyCL architecture in a 65 nm CMOS
technology node with the conventional ASIC design flow. It executes 1 epoch of
training on a Conv + ReLU + Dense model on the CIFAR10 dataset in 1.76 s, while
1 training epoch of the same model using an Nvidia Tesla P100 GPU takes 103 s,
thus achieving a 58x speedup, consuming 86 mW in a 4.74 mm2 die.
