Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. Further, many of them will have stringent size constraints. With no cord for power and limited space for a battery, powering these devices (to achieve several months to possibly years of unattended operation) becomes a daunting challenge. This paper highlights some promising directions for addressing this challenge, focusing on three main building blocks: (a) the design of ultra-low power hardware platforms that integrate computing, sensing, storage, and wireless connectivity in a tiny form factor, (b) the development of intelligent system-level power management techniques, and (c) the use of environmental energy harvesting to make IoT devices self-powered, thus decreasing -- in some cases, even eliminating -- their dependence on batteries. We discuss these building blocks in detail and illustrate case-studies of systems that use them judiciously, including the QUBE wireless embedded platform, which exploits the characteristics of emerging non-volatile memory technologies to seamlessly and efficiently enable long-running computations in systems that experience frequent power loss (i.e., intermittently powered systems).

It is projected that, within the coming decade, there will be more than 50 billion smart objects connected to the Internet of Things (IoT). These smart objects, which connect the physical world with the world of computing infrastructure, are expected to pervade all aspects of our daily lives and revolutionize a number of application domains such as healthcare, energy conservation, transportation, etc. In this paper, we present an overview of the challenges involved in designing energy-efficient IoT edge devices and describe recent research that has proposed promising solutions to address these challenges. First, we outline the challenges involved in efficiently supplying power to an IoT device. Next, we discuss the role of emerging memory technologies in making IoT devices energy-efficient. Finally, we discuss the potential impact that approximate computing can have in increasing the energy-efficiency of wearables and other compute-intensive IoT devices.

Regulation services (RS) play an important role in maintaining the stability of electric grids by correcting for short-term mismatches between electricity generation and demand. RS providers dynamically supply electricity to the grid or consume electricity from it, in response to regulation signals, in return for economic compensation. This capability is commonly realized through large-scale electrical energy storage (EES) systems based on batteries. However, the highly transient nature of the regulation signals implies that the batteries used for RS are subject to frequent charge and discharge cycles, leading to shortened battery life and thereby impacting the profitability of RS. In this work, we explore the use of hybrid EES (HEES) systems, which combine batteries and supercapacitors, to improve the profitability of RS. HEES systems have the potential to reduce the cost of providing RS by utilizing supercapacitors to respond to the high-frequency components of the regulation signal, prolonging battery life. However, realizing this potential presents several challenges. First, the benefits of HEES systems have a profound dependence on the type of hybrid topology (i.e., active or passive), which results in a tradeoff between the implementation cost and the utilization of the supercapacitor capacity. Second, the allocation of energy storage capacity to batteries and supercapacitors should be carefully determined in the design phase because the reduction in battery replacement cost due to the use of supercapacitors must be balanced against the increased upfront cost for supercapacitors. Third, active HEES systems involve the problem of managing the power flows to batteries and supercapacitors so as to realize maximum cost benefits. To address these challenges, we present a framework for the design and management of a HEES system, so as to maximize the profit from the perspective of an RS provider. This framework consists of i) a design-time capacity optimization phase that determines the best allocation of capacity to batteries and supercapacitors and ii) a run-time management scheme that selects how the different storage devices are orchestrated considering their characteristics and the incoming regulation signal. Our experiments show that, with the proposed capacity optimization and management framework, the use of a passive or an active HEES system can improve the profit of RS providers by 1.16× or 5.44×, respectively.

General matrix multiplication (GEMM) is universal in various applications, such as signal processing, machine learning, and computer vision. Conventional GEMM hardware architectures based on binary computing exhibit low area and energy efficiency as they scale due to the spatial nature of number representation and computing. Unary computing, on the other hand, can be performed with extremely simple processing units, often just with a single logic gate. But currently there exist no efficient architectures for unary GEMM.In this paper, we present uGEMM, an area- and energy-efficient unary GEMM architecture enabled by novel arithmetic units. The proposed design relaxes previously-imposed constraints on input bit streams—low correlation and long stream length— and achieves superior area and energy efficiency over existing unary systems. Furthermore, uGEMM’s output bit streams exhibit higher accuracy and faster convergence, enabling dynamic energy-accuracy scaling on resource-constrained systems.