IC Colloquium : Disciplined Approximate Computing: From Language to Hardware
Event details
| Date | 25.01.2013 |
| Hour | 16:15 › 17:30 |
| Speaker | Luis Ceze, University of Washington |
| Location | |
| Category | Conferences - Seminars |
Abstract
Energy is increasingly a first-order concern in computer systems. Exploiting energy-accuracy trade-offs is an attractive choice in applications that can tolerate inaccuracies. Recent work has explored exposing this trade-off in programming models. A key challenge, though, is how to isolate parts of the program that must be precise from those that can be approximated so that a program functions correctly even as quality of service degrades.
In this talk I will described our effort on co-designing language and hardware to take advantage of approximate computing for significant energy savings. We use type qualifiers to declare data that may be subject to approximate computation. Using these types, the system automatically maps approximate variables to low-power storage, uses low-power operations, and even applies more energy-efficient algorithms provided by the programmer. In addition, the system can statically guarantee isolation of the precise program component from the approximate component. This allows a programmer to control explicitly how information flows from approximate data to precise data. Importantly, employing static analysis eliminates the need for dynamic checks, further improving energy savings. I will describe a micro-architecture that offers explicit approximate storage and computation and our recent proposal on using neural networks as approximate accelerators for imperative programs. I will conclude with an overview of our current/future research directions, including language extensions for quality-of-result specification, programming tools,
approximate storage and approximate wireless communication.
Biography
Luis Ceze is an Associate Professor in the Computer Science and Engineering Department at the University of Washington. His research focuses on computer architecture, programming languages and OS to improve the programmability, reliability and energy efficiency of multiprocessor systems. He has co-authored over 60 papers in these areas, and had several papers selected as IEEE Micro Top Picks and CACM research Highlights. He participated in the Blue Gene, Cyclops,
and PERCS projects at IBM and is a recipient of IBM awards. He is also a recipient of an NSF CAREER Award, a Sloan Research Fellowship and a Microsoft Research Faculty Fellowship. He consults for Microsoft Research and co-founded Corensic, a UW-CSE spin-off company. He was born and raised in Sao Paulo, Brazil, where it drizzles all the time; he now lives in the similarly drizzly Seattle. When he is not working he is found either eating or cooking.
Energy is increasingly a first-order concern in computer systems. Exploiting energy-accuracy trade-offs is an attractive choice in applications that can tolerate inaccuracies. Recent work has explored exposing this trade-off in programming models. A key challenge, though, is how to isolate parts of the program that must be precise from those that can be approximated so that a program functions correctly even as quality of service degrades.
In this talk I will described our effort on co-designing language and hardware to take advantage of approximate computing for significant energy savings. We use type qualifiers to declare data that may be subject to approximate computation. Using these types, the system automatically maps approximate variables to low-power storage, uses low-power operations, and even applies more energy-efficient algorithms provided by the programmer. In addition, the system can statically guarantee isolation of the precise program component from the approximate component. This allows a programmer to control explicitly how information flows from approximate data to precise data. Importantly, employing static analysis eliminates the need for dynamic checks, further improving energy savings. I will describe a micro-architecture that offers explicit approximate storage and computation and our recent proposal on using neural networks as approximate accelerators for imperative programs. I will conclude with an overview of our current/future research directions, including language extensions for quality-of-result specification, programming tools,
approximate storage and approximate wireless communication.
Biography
Luis Ceze is an Associate Professor in the Computer Science and Engineering Department at the University of Washington. His research focuses on computer architecture, programming languages and OS to improve the programmability, reliability and energy efficiency of multiprocessor systems. He has co-authored over 60 papers in these areas, and had several papers selected as IEEE Micro Top Picks and CACM research Highlights. He participated in the Blue Gene, Cyclops,
and PERCS projects at IBM and is a recipient of IBM awards. He is also a recipient of an NSF CAREER Award, a Sloan Research Fellowship and a Microsoft Research Faculty Fellowship. He consults for Microsoft Research and co-founded Corensic, a UW-CSE spin-off company. He was born and raised in Sao Paulo, Brazil, where it drizzles all the time; he now lives in the similarly drizzly Seattle. When he is not working he is found either eating or cooking.
Links
Practical information
- Informed public
- Free
- This event is internal
Organizer
- Prof. George Candea
Contact
- Christine Moscioni