YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithmetic for Large-Scale AI
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Abstract (arXiv)
In this paper, we further explore the potential of analog in-memory computing (AiMC) and introduce an innovative artificial intelligence (AI) accelerator architecture named YOCO, featuring three key proposals: (1) YOCO proposes a novel 8-bit in-situ multiply arithmetic (IMA) achieving 123.8 TOPS/W energy-efficiency and 34.9 TOPS throughput through efficient charge-domain computation and timedomain accumulation mechanism. (2) YOCO employs a hybrid ReRAM-SRAM memory structure to balance computational efficiency and storage density. (3) YOCO tailors an IMC-friendly attention computing flow with an efficient pipeline to accelerate the inference of transformer-based AI models. Compared to three SOTA baselines, YOCO on average improves energy efficiency by up to 3.9x-19.9x and throughput by up to 6.8x-33.6x across 10 CNN/transformer models.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 19 Dec 2023.
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4 of 19 references.
References · 4
- 2017Attention Is All You Needcited by 60,361
- 2023Artificial Intelligence Index Report 2023cited by 13
- 2023RAELLA: Reforming the Arithmetic for Efficient, Low-Resolution, and Low-Loss Analog PIM: No Retraining Required!cited by 9
- 2021CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inferencecited by 5
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Citing papers per year: 2026: 1.
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