Extending Moore’s Law via Computationally Error-Tolerant Computing
doi: 10.1145/3177837
Extending Moore’s Law via Computationally Error-Tolerant Computing
Dennard scaling has ended. Lowering the voltage supply ( V dd ) to sub-volt levels causes intermittent losses in signal integrity, rendering further scaling (down) no longer acceptable as a means to lower the power required by a processor core. However, it is possible to correct the occasional errors caused due to lower V dd in an efficient manner and effectively lower power. By deploying the right amount and kind of redundancy, we can strike a balance between overhead incurred in achieving reliability and energy savings realized by permitting lower V dd . One promising approach is the Redundant Residue Number System (RRNS) representation. Unlike other error correcting codes, RRNS has the important property of being closed under addition, subtraction and multiplication, thus enabling computational error correction at a fraction of an overhead compared to conventional approaches. We use the RRNS scheme to design a Computationally-Redundant, Energy-Efficient core, including the microarchitecture, Instruction Set Architecture (ISA) and RRNS centered algorithms. From the simulation results, this RRNS system can reduce the energy-delay-product by about 3× for multiplication intensive workloads and by about 2× in general, when compared to a non-error-correcting binary core.
- Georgia Institute of Technology United States
- Sandia National Laboratories California United States
- Sandia National Laboratories United States
17 Research products, page 1 of 2
- 2017IsAmongTopNSimilarDocuments
- 1999IsAmongTopNSimilarDocuments
- 2000IsAmongTopNSimilarDocuments
- 2022IsAmongTopNSimilarDocuments
- 2013IsAmongTopNSimilarDocuments
- 1995IsAmongTopNSimilarDocuments
- 1994IsAmongTopNSimilarDocuments
- 2011IsAmongTopNSimilarDocuments
chevron_left - 1
- 2
chevron_right
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).8 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
