Improving Power of DSP and CNN Hardware Accelerators Using Approximate Floating-point Multipliers
| Autor: | Dimitrios Soudris, Kiamal Pekmestzi, Vasileios Leon, Theodora Paparouni, Evangelos Petrongonas |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Power gain Floating point business.industry Computer science 02 engineering and technology 01 natural sciences 020202 computer hardware & architecture Reduction (complexity) Significand Hardware and Architecture Approximation error 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Overhead (computing) Multiplication Multiplier (economics) business Software Computer hardware |
| Zdroj: | ACM Transactions on Embedded Computing Systems. 20:1-21 |
| ISSN: | 1558-3465 1539-9087 |
| Popis: | Approximate computing has emerged as a promising design alternative for delivering power-efficient systems and circuits by exploiting the inherent error resiliency of numerous applications. The current article aims to tackle the increased hardware cost of floating-point multiplication units, which prohibits their usage in embedded computing. We introduce AFMU (Approximate Floating-point MUltiplier), an area/power-efficient family of multipliers, which apply two approximation techniques in the resource-hungry mantissa multiplication and can be seamlessly extended to support dynamic configuration of the approximation levels via gating signals. AFMU offers large accuracy configuration margins, provides negligible logic overhead for dynamic configuration, and detects unexpected results that may arise due to the approximations. Our evaluation shows that AFMU delivers energy gains in the range 3.6%–53.5% for half-precision and 37.2%–82.4% for single-precision, in exchange for mean relative error around 0.05%–3.33% and 0.01%–2.20%, respectively. In comparison with state-of-the-art multipliers, AFMU exhibits up to 4–6× smaller error on average while delivering more energy-efficient computing. The evaluation in image processing shows that AFMU provides sufficient quality of service, i.e., more than 50 db PSNR and near 1 SSIM values, and up to 57.4% power reduction. When used in floating-point CNNs, the accuracy loss is small (or zero), i.e., up to 5.4% for MNIST and CIFAR-10, in exchange for up to 63.8% power gain. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|