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초록

Transformer models have revolutionized artificial intelligence (AI) applications across various domains, but their increasing complexity poses significant challenges in terms of computational and memory demands. While processing-in-memory (PIM) paradigms have been adopted to address these limitations, existing PIM-based transformer accelerators still face hurdles such as: 1) focusing solely on optimizing attention layers; 2) lack of sparsity exploitation for transformers; and 3) limited PIM macro capacity and low cell density, which degrades on-chip data reuse and increases external memory access (EMA). This article presents DPIM, a novel 2T1C eDRAM-based transformer-in-memory chip that addresses these challenges through three key innovations: 1) a sparsity-aware quantization (SAQ) scheme that significantly increases bit-slice sparsity in both activation and weight data, achieving ratios of 83.3% and 88.4%, respectively, with minimal accuracy loss; 2) a heterogeneous PIM core capable of efficiently handling both sparse and dense matrix multiplications (MMs); and 3) a high-density 2T1C eDRAM cell with a density of 1.38 Mb/mm(2), enabling large-capacity PIM macros. By integrating these features, DPIM achieves improved computational efficiency and reduced EMA with enhanced on-chip data reuse. The DPIM chip, fabricated using 28-nm CMOS technology, achieves a throughput of 3.03-12.12 TOPS and an energy efficiency of 4.84-19.36 TOPS/W, all measured across INT8 and INT4 operations, respectively. It achieves a throughput density of 0.55 TOPS/mm(2) with INT8 operation. With a total macro size of 4608 kb, the chip occupies a die area of 20.25 mm(2) and operates at frequencies from 50 to 285 MHz with a supply voltage of 0.85-1.0 V. The DPIM successfully executes BERT-Large on the general language understanding evaluation (GLUE) dataset. Its macro density is 1413 kb/mm(2), and the resulting density figure-of-merit (FoM) (macro density x throughput density) is 1.6x - 115.8x higher than previous works, representing a significant advancement in hardware design for efficient transformer processing.

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