# YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithmetic for Large-Scale AI

arXiv: https://arxiv.org/abs/2312.11836 · HTML: https://mlsanity.com/paper/2312.11836/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Artificial Intelligence Index Report 2023](https://mlsanity.com/paper/2310.03715/) (2023)
- [RAELLA: Reforming the Arithmetic for Efficient, Low-Resolution, and Low-Loss Analog PIM: No Retraining Required!](https://mlsanity.com/paper/2304.07935/) (2023)
- [CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference](https://mlsanity.com/paper/2107.02388/) (2021)

## Built on by (most cited first, 1 of 1)

- [You Only Charge Once 2.0 : A End-to-End Analog Computing-in-Memory Architecture with Reconfigurable Switched Capacitors](https://mlsanity.com/paper/2608.11116/) (2026)

## 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.