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Sriram.Vajapeyam
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Inexact processor is more power efficient
Peter Clarke
5/21/2012 4:59 AM EDT
LONDON – Processing circuitry that has been designed to allow imprecision has been shown to be 15 times more power efficient than conventional circuitry at performing some tasks. The technique is particularly applicable in audio and graphical subsystems and such circuits could start showing up in hearing aids and tablet computers in 2013, researchers said.
A prototype processor was built by a team of researchers from Rice University in Houston, Singapore’s Nanyang Technological University (NTU), Switzerland’s Center for Electronics and Microtechnology (CSEM) and the University of California, Berkeley and reported on at the ACM International Conference on Computing Frontiers in Cagliari, Italy, where it won best paper.
The concept, which has echoes of fuzzy logic and probablistic processing, is straightforward: allow hardware for operations such as multiplication and adding to make mistakes but manage the probability of errors building up.
The team has used a number of techniques to deviate from conventional full-precision, absolute accuracy circuits. These include "pruning" where the team cuts away rarely used portions of a digital circuit and "confined voltage scaling."
In initial simulations published in 2011, the team showed how "pruned" sections of conventionally designed chips could run twice as fast and be half the size and use half the energy of the originals. In the latest research the team has implemented their ideas on a prototype silicon chip.
"In the latest tests, we showed that pruning could cut energy demands 3.5 times with chips that deviated from the correct value by an average of 0.25 percent," said study co-author Avinash Lingamneni, a Rice graduate student. "When we factored in size and speed gains, these chips were 7.5 times more efficient than regular chips. Chips that got wrong answers with a larger deviation of about 8 percent were up to 15 times more efficient."
Christian Enz, who leads the CSEM arm of the collaboration, said: "Particular types of applications can tolerate quite a bit of error. For example, the human eye has a built-in mechanism for error correction. We used inexact adders to process images and found that relative errors up to 0.54 percent were almost indiscernible, and relative errors as high as 7.5 percent still produced discernible images."
Project leader Krishna Palem, who also serves as director of the Rice-NTU Institute for Sustainable and Applied Infodynamics (ISAID) said initial applications for pruning are likely to be in application-specific processors, used in hearing aids, cameras and other electronic devices.
Inexact hardware is also being considered for ISAID's I-slate educational tablet, which is designed for Indian classrooms. Pruned chips are expected to cut power requirements in half and allow the I-slate to run from small solar panels similar to those used on handheld calculators. Palem said the first I-slates and prototype hearing aids to contain pruned chips are expected by 2013.
Related links and articles:
www.rice.edu
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Paul A. Clayton
5/21/2012 9:03 AM EDT
While approximate computation may be most readily used for processing data for human sensory input, it could also be useful for certain test-and-confirm type problems (where an approximate test--not even necessarily absolutely excluding false negatives--can filter out a majority of uninteresting results [distributed computing projects tend to work this way, the Large Hadron Collider also uses data filtering which _might_ be amenable to approximation if the false negative probability was sufficiently low]).
Something like a search engine could probably use approximate computation (the result is a list sorted by estimated fitness).
It might be possibly to increase the effectiveness of a safety system by allowing the use of more data and more processing even though the processing is approximate. Likewise, a self-correcting system (e.g., a flight control system) could tolerate minor errors.
Much simulation is effectively approximate (e.g., modelling of the gravity of distant objects as a single point object) already. In addition, measurements are inherently approximate and incomplete, so using approximate computation might be less inaccurate than one might naively expect.
Error detection and correction are also probabilistic, and so might be able to use approximate computation (Lyric Semiconductor's ECC product?).
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agk
5/21/2012 9:58 AM EDT
In exact processor may be equal to a regular processor when the no of bits are reduced. So less power consumption and higher speed.
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Doug@RCW
5/21/2012 3:50 PM EDT
This reminds me of college courses that described the notion of significant digits. If I take a measurement accuracy of 0.1 , and then mash a number data points together and apply statistical methods, my answer will never be more accurate 0.1 or even less. Computers are stupid with arithmetic and will give me 10 decimal points to the right. And then keep these answers and process all these digits to compute a new answer. Audio and video processing generate data that is interpreted by human analog processes that greatly smooth out the results. Sound is measured with a logarithmic scale. I defy anyone human to detect less than 1 dB of sound pressure. And computers measure millivolts, not dB. We can save a lot of data storage by compressing the raw data reducing the number of bits to manage.
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Frank Eory
5/21/2012 7:41 PM EDT
There are a huge number of applications that don't require extreme accuracy and could take advantage of this.
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daleste
5/22/2012 10:12 PM EDT
This is very interesting. I think it will have an impact in power consumption of future devices. I don't mind a little less precision for the display or sound, but I hope it will still balance my checkbook correctly.
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Chuck(G)
5/25/2012 8:03 PM EDT
Variable precision?
Are these people seeking to re-invent the IBM 1620?
Maybe the old ideas aren't so old after all.
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Sriram.Vajapeyam
6/18/2012 7:07 AM EDT
It is great to see this idea applied to processors!
This idea has applications even for scientific computing, albeit at the shared-memory/message-passing level. The book "Parallel and Distributed Computation: Numerical Methods" by Bertsekas and Tsitsiklis of MIT identifies asynchronous/partially-asynchronous parallel iterative algorithms that converge to correct solutions even if their intermediate results are not exchanged in timely fashion. One could go from there to partially-accurate intermediate results rather than non-timely intermediate results.
Way back in the mid-1990s, we exploited this to propose non-strict cache coherence/message passing for parallel processors, to speed up parallel programs (HiPC 1996: "Program-Level Control of Network Delay for Parallel Asynchronous Iterative Applications", http://dl.acm.org/citation.cfm?id=822137). Today that would help power savings as well! Later we looked at modern applications that leverage genetic algorithms, etc (ICPP 2000, ISCA WSHMM Workshop 1999, etc). Subsequent to our 1996 HiPC paper, folks at Georgia Tech and erstwhile DEC CRL applied the idea to parallel multimedia applications -- they built a system called Beehive I think.
It is great that the idea has now reached the processor level. I recall when I first talked about my version of the idea to a few colleagues in the mid-1990s. They looked at me like I didn't know the basics of science!
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