Developing energy efficient computing is a high priority worldwide, particularly as the race towards exascale computing heats up. As is often noted, the human brain does with 10-20 watts what supercomputers require megawatts to accomplish. This week the National Science Foundation and Semiconductor Research Corporation funded nine projects aimed at developing more energy efficient computers.
“Only disruptive breakthroughs can enable computers to perform as the human brain does, in terms of problem-solving capability and lower power, which, for the human brain, is less than a light bulb’s worth of consumption,” said Dimitris Pavlidis, NSF’s Directorate for Engineering program director for the Energy-Efficient Computing: from Devices to Architectures (E2CDA) initiative.
The awards, totaling $21.6M, are for three-year projects which will simultaneously work on novel approaches — including developing nanoscale devices and materials and integrating them into three-dimensional systems — while inventing new computer architectures to process, store and communicate data.
The nine E2CDA projects are:
- 2D Electrostrictive FETs for Ultra-Low Power Circuits and Architectures, Saptarshi Das, Pennsylvania State University
- A Fast 70 mV Transistor Technology for Ultra-Low-Energy Computing, Benton Calhoun, University of Virginia; Mykhailo Povolotskyi, Purdue University; and Mark Rodwell, University of California, Santa Barbara
- Electronic-Photonic Integration Using the Transistor Laser for Energy-Efficient Computing, John Dallesasse, University of Illinois at Urbana-Champaign; and Yanjing Li, University of Chicago
- Energy Efficient Computing with Chip-Based Photonics, Yeshaiahu Fainman, University of California, San Diego; Alexander Gaeta, Columbia University; Benjamin Lev, Stanford University; and Marin Soljacic, Massachusetts Institute of Technology
- Energy Efficient Learning Machines, Subhasish Mitra, Stanford University; and Sayeef Salahuddin, University of California, Berkeley
- Excitonic Devices, Leonid Butov, University of California, San Diego
- EXtremely Energy Efficient Collective ELectronics, Suman Datta, University of Notre Dame
- Memory, Logic and Logic in Memory Using Three Terminal Magnetic Tunnel Junctions, Marc Baldo, Massachusetts Institute of Technology
- Self-Adaptive Reservoir Computing with Spiking Neurons: Learning Algorithms and Processor Architectures, Peng Li, Texas A&M Engineering Experiment Station
In addition to the Nanotechnology-Inspired Grand Challenge for Future Computing, the jointly supported research effort aligns with the National Strategic Computing Initiative and other interagency initiatives and priorities according to NSF.
Link to NSF announcement: https://www.nsf.gov/news/news_summ.jsp?cntn_id=190060&org=NSF&from=news