Director of Research (if dissertation) or Advisor (if thesis)
Kim, Nam Sung
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Approximate computing
Computer architecture
Abstract
As Moore's law continues to decline, diminishing benefits of transistor scaling necessitate a move towards specialized hardware. Approximate computing is one type of specialization that has shown promise in improving the efficiency of general-purpose processors. Fortunately, with increasing demand for data collection and processing across industry, a wide range of modern applications operate on real-world data with properties suitable for approximation. To exploit data patterns and repetitions in these applications, we propose Approximate Algebraic Memory (A2M), a specialized memory model that uses finite degree polynomials to approximate discrete ranges of memory data. A2M uses dedicated hardware to derive and store polynomial coefficients rather than memory data. In error resilient workloads, A2M can effectively reduce memory size and enable direct computation on memory content. We evaluate an on-chip implementation of A2M for general-purpose processors. Experiment results show that for CPU workloads, A2M yields minimal error (< 1%) at a fixed compression ratio of 16, and improves performance by 11.3% on average.
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