Adrian Sampson, Jacob Nelson, Karin Strauss, and Luis Ceze
9 December 2013
Memories today expose an all-or-nothing correctness model that incurs significant costs in performance, energy, area, and design complexity. But not all applications need high-precision storage for all of their data structures all of the time. This paper proposes mechanisms that enable applications to store data approximately and shows that doing so can improve the performance, lifetime, or density of solid-state memories. We propose two mechanisms. The first allows errors in multi-level cells by reducing the number of programming pulses used to write them. The second mechanism mitigates wear-out failures and extends memory endurance by mapping approximate data onto blocks that have exhausted their hardware error correction resources. Simulations show that reduced-precision writes in multi-level phase-change memory cells can be 1.7 faster on average and using failed blocks can improve array lifetime by 23% on average with quality loss under 10%.
|Published in||MICRO-46 (International Symposium on Microarchitecture)|
Adrian Sampson, Jacob Nelson, Karin Strauss, and Luis Ceze. Approximate Storage in Solid-State Memories, Transactions on Computer Systems, ACM – Association for Computing Machinery, September 2014.