Earables such as earphones [15, 16, 73], hearing aids [28], and smart glasses [2, 14] are poised to be a prominent programmable computing platform in the future. In this paper, we ask the question: what kind of programmable hardware would be needed to support earable computing in future? To understand hardware requirements, we propose EarBench, a suite of representative emerging earable applications with diverse sensor-based inputs and computation requirements. Our analysis of EarBench applications shows that, on average, there is a 13.54×-3.97× performance gap between the computational needs of EarBench applications and the performance of the microprocessors that several of today's programmable earable SoCs are based on; more complex microprocessors have unacceptable energy efficiency for Earable applications. Our analysis also shows that EarBench applications are dominated by a small number of digital signal processing (DSP) and machine learning (ML)-based kernels that have significant computational similarity. We propose SpEaC --- a coarse-grained reconfigurable spatial architecture - as an energy-efficient programmable processor for earable applications. SpEaC targets earable applications efficiently using a) a reconfigurable fixed-point multiply-and-add augmented reduction tree-based substrate with support for vectorized complex operations that is optimized for the earable ML and DSP kernel code and b) a tightly coupled control core for executing other code (including non-matrix computation, or non-multiply or add operations in the earable DSP kernel code). Unlike other CGRAs that typically target general-purpose computations, SpEaC substrate is optimized for energy-efficient execution of the earable kernels at the expense of generality. Across all our kernels, SpEaC outperforms programmable cores modeled after M4, M7, A53, and HiFi4 DSP by 99.3×, 32.5×, 14.8×, and 9.8× respectively. At 63 mW in 28 nm, the energy efficiency benefits are 1.55 ×, 9.04×, 68.3 ×, and 32.7 × respectively; energy efficiency benefits are 15.7 × -- 1087 × over a low power Mali T628 MP6 GPU.

Node movement can be exploited to reduce the energy consumption of wireless network communication. The strategy consists in delaying communication until a mobile node moves close to its target peer node within an application- imposed deadline. We evaluate the performance of various heuristics that, based on the movement history of the mobile node, estimate an optimal time (in the sense of least energy use) of communication subject to the delay constraint. We evaluate the impact of the node movement model, length of movement history maintained, allowable delay, single hop versus multiple hop communication, and size of data transfer on the energy consumption. We also present measurement results on an iPAQ pocket PC that quantity energy consumption in executing the prediction algorithms. Our results show that, with relatively simple and, hence, efficient prediction heuristics, energy savings in communication can significantly outweigh the energy expenses in executing the prediction algorithms. Moreover, it is possible to achieve robust system performance across diverse node movement models.

Node movement can be exploited to reduce the energy consumption of wireless network communication. The strategy consists in delaying communication until a mobile node moves close to its target peer node, within an application-imposed deadline. We evaluate the performance of various heuristics that, based on the movement history of the mobile node, estimate an optimal time (in the sense of least energy use) of communication subject to the delay constraint. We evaluate the impact of node movement model, length of movement history maintained, allowable delay, single hop versus multiple hop communication, and size of data transfer on the energy consumption. We also present measurement results on an iPAQ pocket PC that quantify energy consumption in executing the prediction algorithms. Our results show that, with relatively simple and hence efficient prediction heuristics, energy savings in communication can significantly outweigh the energy expenses in executing the prediction algorithms.

One of the principal concerns of wireless computing is the limited battery life of mobile devices. To address the concern, application-aware adaptation has emerged as one of the effective strategies for controlling energy usage. But for adaptation to be effective, we must be able to predict the energy requirement of running applications, in an application-specific manner. This paper reports a set of experiments performed to understand the energy usage pattern of handheld devices while decoding and displaying MPEG compressed video in software. Experiments are designed to bring forth parameters that can be used to predict the energy requirement for MPEG playback. Based on our experimental results, we find that it is possible to construct simple polynomial models of energy usage with good least square fit (R value of 0.92 and higher). These models can be used to predict application energy requirement under various combinations of controllable video parameters.