CADE: Configurable Approximate Divider for Energy Efficiency
Mohsen Imania, Ricardo Garciab, Andrew Huangc and Tajana Rosingd
Computer Science and Engineering Department, UC San Diego, La Jolla, USA
amoimani@ucsd.edu
brag023@ucsd.edu
canh162@ucsd.edu
dtajana@ucsd.edu
ABSTRACT
Approximate computing is a promising solution to design faster and more energy efficient systems, which provides an adequate quality for a variety of functions. Division, in particular, floating point division, is one of the most important operations in multimedia applications, which has been implemented less in hardware due to its significant cost and complexity. In this paper, we proposed CADE, a Configurable Approximate Divider which performs floating point division operation with a runtime controllable accuracy. The approximation of the CADE is accomplished by removing the costly division operation and replacing it with a subtraction of the input operands mantissa. To increase the level of accuracy, CADE analyses the first N bits (called tuning bits) of both input operands mantissa to estimate the division error. If CADE determines that the first approximation is unacceptable, a pre-computed value is retrieved from memory and subtracted from the first approximation mantissa. At runtime, CADE can provide a higher accuracy by increasing the number of tuning bits. The proposed CADE was integrated on the AMD GPU architecture. Our evaluation shows that CADE is at least 4.1× more energy efficient, 1.5× faster, and 1.7× higher area efficient as compared to state-of the-art approximate dividers while providing 25% lower error rate. In addition, CADE gives a new knob to GPU in order to configure the level of approximation at runtime depending on the application/user accuracy requirement.
Keywords: Approximate computing, Energy-efficiency, GPGPU.
