{TinyRCE}: Multi Purpose Forward Learning for Resource Restricted Devices

The challenge of deploying neural network (NN) learning workloads on ultralow power tiny devices has recently attracted several machine learning researchers of the Tiny machine learning community. A typical on-device learning session processes real-time streams of data acquired by heterogeneous sensors. In such a context, this letter proposes Tiny Restricted Coulomb energy (TinyRCE), a forward-only learning approach based on a hyperspherical classifier, which can be deployed on microcontrollers and potentially integrated into the sensor package. TinyRCE is fed with compact features extracted by a convolutional neural network (CNN), which can be trained with backpropagation or it can be an extreme learning machine with randomly initialized weights. A forget mechanism has been introduced to discard useless neurons from the hidden layer, since they can become redundant over time. TinyRCE has been evaluated with a new interleaved learning and testing data protocol to mimic a typical forward on-tiny-device workload. It has been tested with the standard MLCommons Tiny datasets used for keyword spotting and image classification, and against the respective neural benchmarks. In total, 95.25% average accuracy was achieved over the former classes (versus 91.49%) and 87.17% over the latter classes (versus 100%, caused by overfitting). In terms of complexity, TinyRCE requires 22x less Multiply and ACCumulate (MACC) than SoftMax (with 36 epochs) on the former, whereas it requires 5x more MACC than SoftMax (with 500 epochs) for the latter. Classifier complexity and memory footprint are marginal w.r.t. the feature extractor, for training and inference workloads.