Did you hear the one about how many batteries it takes to turn on a Turing machine? None! It’s outside the model of computation. Yet it’s extremely difficult to store information or compute without power. Perpetual computing is hard. As embedded systems continue to shrink in size and energy consumption, the battery becomes the greatest bottleneck. I will describe recent research results on batteryless, RFID-scale computers: the UMass Moo platform, stochastic storage on Half-Wits (USENIX FAST), and energy-aware checkpoints with Mementos (ACM ASPLOS). The UMass Moo is an embedded system based on the Intel WISP. The mixed signal system combines hardware and software to behave like an RFID tag with non-volatile memory, sensing, radio communication, and von Neumann-style computation. This batteryless device operates on RF energy harvesting and uses a small capacitor as a voltage supply. The capacitor stores 100 million times less energy than a typical AA battery. This lack of energy leads to two research challenges: how to reliably store data in non-volatile memory at low cost and low voltage, and how to compute when power losses interrupt programs every few hundred milliseconds. The Half-Wits work analyzes the stochastic behavior of writing to embedded flash memory at voltages lower than recommended by a microcontroller’s specifications to reduce energy consumption. Flash memory integrated within a microcontroller typically requires the entire chip to operate on common supply voltage almost double what the CPU portion requires. Our approach tolerates a lower supply voltage so that the CPU may operate in a more energy efficient manner. Our software-only coding algorithms enable reliable storage at low voltages on unmodified hardware by exploiting the electrically cumulative nature of half-written data in write-once bits. Measurements show that our software approach reduces energy consumption by up to 50%. This work is joint with Erik Learned-Miller (UMass Amherst) and Andrew Jiang (Texas A&M). Mementos helps programs run to completion despite interruptions of power. Transiently powered computers risk the frequent, complete loss of volatile memory. Thus, Mementos automatically instruments programs with energy-aware checkpoints to protect RAM and registers. Mementos consists of a suite of compile- and run-time tools that help to transform long-running programs into interruptible computations. The contributions include a study of the run-time environment for programs on RFID-scale devices, an energy-aware state checkpointing system for MSP430 family of microcontrollers, and a trace-driven simulator of transiently powered RFID-scale devices. This work is joint with Jacob Sorber (Dartmouth College).
Kevin Fu is an Associate Professor of Computer Science and Adjunct Associate Professor of Electrical & Computer Engineering at the University of Massachusetts Amherst. Prof. Fu makes embedded computer systems smarter: better security and safety, reduced energy consumption, faster performance. His most cited contributions pertain to computational RFIDs, trustworthy medical devices, secure storage, and web authentication. His research has been featured in the New York Times, Wall Street Journal, NPR, Boston Globe, Washington Post, LA Times, IEEE Spectrum, Consumer Reports, and several others. Prof. Fu was named MIT Technology Review TR35 Innovator of the Year. He received a Sloan Research Fellowship, NSF CAREER award, and best paper awards from USENIX Security, IEEE Symp. of Security and Privacy, and ACM SIGCOMM. Prof. Fu is an incoming member of the NIST Information Security and Privacy Advisory Board and a visiting scientist at the Food and Drug Administration (FDA). Prof. Fu directs the UMass Amherst Security and Privacy Research lab (spqr.cs.umass.edu), the Open Medical Device Research Library (omdrl.org), and the RFID Consortium on Security and Privacy (RFID-CUSP.org). He is co-director of the Medical Device Security Center (secure-medicine.org). Prof. Fu is a frequent visiting faculty member at Microsoft Research, the MIT Computer Science and Artificial Intelligence Lab, and the Beth Israel Deaconess Medical Center of the Harvard Medical School. Prof. Fu received his Ph.D. in Electrical Engineering and Computer Science from MIT.