RUN-TIME CONFIGURABLE APPROXIMATE MULTIPLIER DESIGN

Abstract

The complexity of arithmetic continues to be an issue in the design of high-performance and energy-efficient hardware. The problem is further exacerbated in systems powered by variable power levels can limit their computation capabilities. Multipliers constitute a major component of these applications with complex logic design and a large gate count compared to other arithmetic units. As such, there is significant interest in designing new approaches to low-complexity multipliers. Recently, approximate arithmetic, in particular approximate adders and multipliers, have shown notable advantages to benefit from a wide spectrum of naturally imprecise-tolerant applications, such as image processing, pattern recognition, and machine learning (ML). The concept of approximate arithmetic involves replacing system components of normal degrees of complexity with less complex components, which may provide reduced accuracy. Compared to the adder, the multiplier is a crucial component of these applications with complex logic design and a large gate count. This thesis investigates the possibility and profitability to trade accuracy for energy at run-time by using configurable approximate arithmetic hardware. In the first approach, a configurable adaptive approximation method for multiplication is proposed. The extra overheads associated with in the configuration circuits prove to be negligible compared to the multiplier’s costs. Central to the proposed approach is a significance-driven logic compression (SDLC) multiplier architecture that can dynamically adjust the level of approximation depending on the run-time power/accuracy constraints. The architecture can be configured to operate in the exact mode (no approximation) or in progressively higher approximation modes (i.e. 2 to 4-bit SDLC). In the second approach, a novel ML hardware design method centred around multiply–accumulate (MAC) units is presented. Core to the configurable MAC design is a configurable multiplier. In the third approach, a configurable modified activation function is proposed to minimize the prediction error of the configurable MAC design. To evaluate and validate the trade-offs, the three approaches (configurable multiplier, MAC unit and modified activation function) are designed in System-Verilog and synthesized using Synopsys Design Compiler, employing a UMC 90nm digital complementary metal-oxide semiconductor (CMOS) technology as well as on Field Programmable Gate Arrays (FPGAs), and then compared with other available methods. These improvements come at the expense of errors introduced into the circuit and investigated. The efficacy of the first approach (configurable multiplier) technique is evaluated with a real life image processing application, which consists of additions and multiplications using the proposed three multiplier configurations (Exact, 2- and 4-bit SDLC). The analysis considers the Gaussian blur filter since it is widely used in image processing application, typically to reduce image noise and artifacts by acting as a low-pass filter. Additionally, the second and third approaches are evaluated as the key processing blocks in a multi-layer perceptron (MLP) network in order to validate the dynamic tunability between accuracy and power consumption. As case studies, the MLP is trained using well-known machine learning (ML) datasets. The configurable multiplier design (first approach) can be suitably used for energy-efficient multiplier designs, where quality requirements can be relaxed. The second and the third approaches (configurable MAC unit and activation function) can also be used within the power-adaptive neuron modules with a minimal loss in output quality compared to those used in previous studies.