qLUT: Input-Aware Quantized Table Lookup for Energy-Efficient Approximate Accelerators

Approximate computing has emerged as a popular design paradigm for optimizing the performance and energy consumption of error-resilient applications in domains such as machine learning, graphics, data analytics, etc. Numerous techniques for approximate computing have been proposed at different layers of the system stack, from circuits to architecture to software. In this work, we propose a new technique, called quantized table lookup, for approximating the meta-functions used in the core computational kernels of error-resilient applications. In contrast to prior work that directly approximates the functionality of the meta-functions, the proposed technique instead approximates the input data to the meta-functions by reducing/quantizing them to a much smaller set of values that we call quantized inputs. The small number of quantized inputs enables us to completely replace the energy-intensive arithmetic units in the meta-function with small and energy-efficient lookup tables (called quantized lookup tables or qLUT) that contain precomputed output values corresponding to the quantized inputs. The proposed approximation technique is not only highly generic, but also inherently quality-configurable and input-aware. Quality-configurability and input-awareness are achieved by modulating the size of the qLUT as well as selecting the values of the quantized inputs judiciously based on the statistics of the original input data. To evaluate the proposed technique, we have implemented the dominant meta-functions of nine error-resilient application benchmarks as quantized table lookup based hardware accelerators using 45nm technology. Experimental results demonstrate average energy savings of 46% at the application-level for minimal (<1%) loss in output quality.