HIGH-PERFORMANCE ACCURATE AND APPROXIMATE MULTIPLIERS FOR FPGA-BASED HARDWARE ACCELERATORS

Multiplication is one of the widely used arithmetic operations in a variety of applications, such as image/video processing and machine learning. FPGA vendors provide high-performance multipliers in the form of DSP blocks. These multipliers are not only limited in number and have fixed locations on FPGAs but can also create additional routing delays and may prove inefficient for smaller bit-width multiplications. Therefore, FPGA vendors additionally provide optimized soft IP cores for multiplication. However, in this work, we advocate that these soft multiplier IP cores for FPGAs still need better designs to provide high-performance and resource efficiency. Towards this, we present generic area-optimized, low-latency accurate and approximate soft core multiplier architectures, which exploit the underlying architectural features of FPGAs, i.e., look-up table (LUT) structures and fast carry chains to reduce the overall critical path delay and resource utilization of multipliers. For illustration, we have deployed the proposed multiplier architecture in accelerators used in image and video applications, and evaluated them for area and performance gains. The design and simulation and effectiveness of the proposed method is synthesized using Xilinx Vivado2018.3.