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Recognizing the explosive increase in the use of AI-based applications,several industrial companies developed custom ASICs (e.g., Google TPU, IBMRaPiD, Intel NNP-I/NNP-T) and constructed a hyperscale cloud infrastructurewith them. These ASICs perform operations of the inference or training processof AI models which are requested by users. Since the AI models have differentdata formats and types of operations, the ASICs need to support diverse dataformats and various operation shapes. However, the previous ASIC solutions donot or less fulfill these requirements. To overcome these limitations, we firstpresent an area-efficient multiplier, named all-in-one multiplier, thatsupports multiple bit-widths for both integer and floating point data types.Then, we build a MAC array equipped with these multipliers with multi-formatsupport. In addition, the MAC array can be partitioned into multiple blocksthat can be flexibly fused to support various DNN operation types. We evaluatethe practical effectiveness of the proposed MAC array by making an acceleratorout of it, named All-rounder. According to our evaluation, the proposedall-in-one multiplier occupies 1.49x smaller area compared to the baselineswith dedicated multipliers for each data format. Then, we compare theperformance and energy efficiency of the proposed All-rounder with threedifferent accelerators showing consistent speedup and higher efficiency acrossvarious AI benchmarks from vision to LLM-based language tasks.

All-rounder: A Flexible AI Accelerator with Diverse Data Format Support and Morphable Structure for Multi-DNN Processing