Adaptive Extreme Edge Computing for Wearable Devices

Abstract

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Ferroelectric-based synapses and neurons for neuromorphic computing

“…Memristive ferroelectric devices have a great potential to be used for weight storage thanks to their nonvolatile properties, but they suffer from limited bit precision and non-linear state change. Nevertheless, some applications, as an example edge computing, already need to cope with tight memory and power constraints, and use therefore lower bit precision [5]. In these cases, adequately high accuracy is ensured using techniques such as synaptic pruning [88], sparse coding [89,90], and stochastic rounding [91].…”

“…The process that leads to a memristive-based neuromorphic system includes undoubtedly many challenges at device level, e.g., optimisation of the materials, scaling of the device, reduction of the device variability, read and write speed, and energy consumption, but also at circuit and system level, e.g., the circuits to address, program, and read the devices and to interface them with the other parts of the chip and with the external world, and the routing of the events [96]. However, to design a hybrid CMOS-memristive hardware, these challenges cannot be tackled separately, they need to be framed in a holistic approach where everything is developed together [5,97]. Moreover, also the learning algorithms should be selected not only according to the intended application, but they also need to be adapted in order to best exploit the features of the memristive devices [5,98].…”

“…However, to design a hybrid CMOS-memristive hardware, these challenges cannot be tackled separately, they need to be framed in a holistic approach where everything is developed together [5,97]. Moreover, also the learning algorithms should be selected not only according to the intended application, but they also need to be adapted in order to best exploit the features of the memristive devices [5,98].…”

“…A possible solution to optimize energy efficiency is to enable computing right next to where the data is collected, e.g., the sensor [4,5]. In this respect, one of the most promising approaches is the neuromorphic approach, in particular the brain-inspired spiking neural network (SNN) [6].…”

Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications

“…Nonetheless, critical challenges still have to be faced, as new constraints must be taken into account when dealing with the limitations imposed by devices intended to be as small and portable as possible [8]. As extensively pointed out in [9], the ultimate goal of edge computing for wearable devices requires a change of paradigm materializing in a reduction of the computational efforts. Typical wearable sensors are indeed affected by severe limitations in terms of power, and the conventional approach-based on data transmission to off-chip, remote servers in charge of processing the acquired signals-introduces an additional limitation on the temporal side.…”