Reducing the Energy Consumption of sEMG-Based Gesture Recognition at the Edge Using Transformers and Dynamic Inference.

Hand gesture recognition applications based on surface electromiographic (sEMG) signals can benefit from on-device execution to achieve faster and more predictable response times and higher energy efficiency. However, deploying state-of-the-art deep learning (DL) models for this task on memory-constrained and battery-operated edge devices, such as wearables, requires a careful optimization process, both at design time, with an appropriate tuning of the DL models' architectures, and at execution time, where the execution of large and computationally complex models should be avoided unless strictly needed. In this work, we pursue both optimization targets, proposing a novel gesture recognition system that improves upon the state-of-the-art models both in terms of accuracy and efficiency.

The Human Brain Project and neuromorphic computing.

Understanding how the brain manages billions of processing units connected via kilometers of fibers and trillions of synapses, while consuming a few tens of Watts could provide the key to a completely new category of hardware (neuromorphic computing systems). In order to achieve this, a paradigm shift for computing as a whole is needed, which will see it moving away from current "bit precise" computing models and towards new techniques that exploit the stochastic behavior of simple, reliable, very fast, lowpower computing devices embedded in intensely recursive architectures. In this paper we summarize how these objectives will be pursued in the Human Brain Project.