The field is witnessing significant advancements in the optimization and efficiency of computational processes, particularly in the realms of deep learning accelerators (DLAs), approximate computing, and matrix multiplication units. Innovations are focusing on reducing energy consumption and latency while maintaining or enhancing accuracy and performance. Tools and algorithms are being developed to explore vast design spaces more efficiently, leveraging data-driven models and transfer learning to bridge the gap between low and high-fidelity evaluation methods. Approximate computing is being combined with in-memory computing to offer solutions that significantly reduce energy consumption and computational steps, applicable in image processing and machine learning tasks. Moreover, the development of exact computation units that exploit sparsity and employ novel encoding techniques is setting new benchmarks in energy efficiency and area-power effectiveness, especially for edge AI applications.

Noteworthy Papers

• Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators: Introduces Starlight, a fast and accurate performance model, and Polaris, a tool that efficiently explores DLA design spaces.
• IMPLY-based Approximate Full Adders for Efficient Arithmetic Operations in Image Processing and Machine Learning: Proposes serial approximate IMPLY-based full adders that significantly reduce energy consumption and computational steps.
• Leveraging Highly Approximated Multipliers in DNN Inference: Presents a control variate approximation technique that enables the use of highly approximate multipliers without retraining, improving inference accuracy and power savings.
• tubGEMM: Energy-Efficient and Sparsity-Effective Temporal-Unary-Binary Based Matrix Multiply Unit: Introduces tubGEMM, a novel matrix-multiply unit design that performs exact GEMM, significantly reducing area, power, and energy consumption.