In emerging technologies, a vital role is played by ASIC designs in processor operations. There is a necessity to develop such a processor composed of low power blocks. This paper discusses the design exploration of the fixed-point multiply-accumulate unit to achieve high-speed and low power consumption. A 2D image convolution process is developed by stacking and combining several MAC blocks. The developed MAC comprises a sequential multiplier, controller, and optimized adder units. The entering image pixels and kernel pixels are checked for similarity and accordingly isolated by the controller unit, thereby saving power by eliminating the redundant multiplications. A novel idea of reducing the additions in image filtering operations is incorporated in the design. The performance of the proposed MAC showed a 28% power reduction compared to the conventional approaches.

A 2D Discrete Cosine Transform and Inverse Discrete Cosine Transform using the B.G. Lee algorithm, incorporating a signed error-tolerant adder for additions, and a signed low-power fixed-point multiplier to perform multiplications are proposed and designed in this research. A novel Application Specific Integrated Circuit hardware implementation is used for the 2D DCT/IDCT computation of each 8 × 8 image block by optimizing the input data using the concepts of pipelining. An enhanced speed in processing and optimized arithmetic computations was observed due to the eight-stage pipeline architecture. The 2D DCT/IDCT of each 8 × 8 image segment can be quickly processed in 34 clock cycles with a substantially reduced level of circuit complexity. The B.G. Lee algorithm has been implemented using signed error-tolerant adders, signed fixed-point multipliers, and shifters, reducing computational complexity, power, and area. The Cadence Genus tool synthesized the proposed architecture with gpdk-90 nm and gpdk-45 nm technology libraries. The proposed method showed a significant reduction of 31.01%, 12.17%, and 21.11% in power, area, and PDP in comparison to the existing image compression architectures. An improved PSNR of the reconstructed image was also achieved compared to existing designs.