Towards Low-Power Machine Learning Architectures Inspired by Brain Neuromodulatory Signalling

Abstract

We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant and a new, similar task is learned by manipulating the firing sensitivity of each neuron via a supplemental bias input. We refer to this as neuromodulatory tuning (NT). We demonstrate empirically that neuromodulatory tuning produces results comparable with traditional fine-tuning (TFT) methods in the domain of image recognition in both feed-forward deep learning and spiking neural network architectures. In our tests, NT reduced the number of parameters to be trained by four orders of magnitude as compared with traditional fine-tuning methods. We further demonstrate that neuromodulatory tuning can be implemented in analog hardware as a current source with a variable supply voltage. Our analog neuron design implements the leaky integrate-and-fire model with three bi-directional binary-scaled current sources comprising the synapse. Signals approximating modulatory neurotransmitter mechanisms are applied via adjustable power domains associated with each synapse. We validate the feasibility of the circuit design using high-fidelity simulation tools and propose an efficient implementation of neuromodulatory tuning using integrated analog circuits that consume significantly less power than digital hardware (GPU/CPU).

Nanomaterial-Based Sensor Array Signal Processing and Tuberculosis Classification Using Machine Learning

“…The diagnosis of TB is an example of the application of ML to small dataset problems. In this paper, our study builds upon prior work [2] and draws inspiration from existing low-power deep learning models [10,11]. Our primary objective is to develop several lowpower ML-based networks to classify the input from nanomaterial-based sensor signals accurately and predict their corresponding labels for diagnosing TB disease.…”