主要关注ISSCC2020至2022，三年内的存算宏和近算宏电路，23虽然advanced program已经出来了，但是毕竟只能看到titile，所以没有把文章整理出来，等论文集放出来后再update，除了被重点关注的SRAM，被报告的还有基于Flash，RRAM，DRAM，PCM，STT-MRAM等多种存储介质的。此外除了存算宏以外，也有processor级的工作。本统计可能存在遗漏，也欢迎指出。
可以将存算/近存宏根据实现原理区分成数字式和模拟式两类，数字式的主要特点为直接将存储器读出结果送入加法器完成后续的计算，而模拟式则要在模拟域完成求和，再通过ADC的方式进行结果转换。
从整理中可以发现的趋势包括：
- 存算/近存算相关的文章数量迅速增加，20年仅有6篇，21年增加到9篇，22年增加到13篇
- 数字式方法增加，从21年被首次提出以来，22年迅速增加到4篇
- 21年开始存算宏作为一个IP合并到ML处理器中，共有4篇工作，22年增加到6篇
一些目前仍然值得聚焦的点：
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对多种精度的支持，包括多定点精度和浮点精度
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外围电路开销问题，模拟式：ADC，数字式：加法树
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稀疏性优化问题
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权重更新问题
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实现片上训练功能
模拟式
20.15.2 A 28nm 64Kb Inference-Training Two-Way Transpose Multibit 6T SRAM Compute-in-Memory Macro for AI Edge Chips
20.15.3 A 351TOPS/W and 372.4GOPS Compute-in-Memory SRAM Macro in 7nm FinFET CMOS for Machine-Learning Applications
20.15.4 A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices
20.15.5 A 28nm 64Kb 6T SRAM Computing-in-Memory Macro with 8b MAC Operation for AI Edge Chips
20.33.1 A 74 TMACS/W CMOS-RRAM Neurosynaptic Core with Dynamically Reconfigurable Dataflow and In-situ Transposable Weights for Probabilistic Graphical Models
20.33.2 A Fully Integrated Analog ReRAM Based 78.4TOPS/W Compute-In-Memory Chip with Fully Parallel MAC Computing
21.15.1 A Programmable Neural-Network Inference Accelerator Based on Scalable In-Memory Computing
21.15.2 A 2.75-to-75.9TOPS/W Computing-in-Memory NN Processor Supporting Set-Associate Block-Wise Zero Skipping and Ping-Pong CIM with imultaneous Computation and Weight Updating
21.15.3 A 65nm 3T Dynamic Analog RAM-Based Computing-in-Memory Macro and CNN Accelerator with Retention Enhancement, Adaptive Analog Sparsity and 44TOPS/W System Energy Efficiency
21.15.4 A 5.99-to-691.1TOPS/W Tensor-Train In-Memory-Computing Processor Using Bit-Level-Sparsity-Based Optimization and Variable-Precision Quantization
21.16.1 A 22nm 4Mb 8b-Precision ReRAM Computing-in-Memory Macro with 11.91 to 195.7TOPS/W for Tiny AI Edge Devices
21.16.2 eDRAM-CIM: Compute-In-Memory Design with Reconfigurable Embedded-Dynamic-Memory Array Realizing Adaptive Data Converters and Charge-Domain Computing
21.16.3 A 28nm 384kb 6T-SRAM Computation-in-Memory Macro with 8b Precision for AI Edge Chips
21.29.1 A 40nm 64Kb 56.67TOPS/W Read-Disturb-Tolerant Compute-in-Memory/Digital RRAM Macro with Active-Feedback-Based Read and In-Situ Write Verification
22.7.5 A 512Gb In-Memory-Computing 3D-NAND Flash Supporting Similar-Vector-Matching Operations on Edge-AI Devices
22.11.2 A 22nm 4Mb STT-MRAM Data-Encrypted Near-Memory Computation Macro with a 192GB/s Read-and-Decryption Bandwidth and 25.1-55.1TOPS/W 8b MAC for AI Operations
22.11.3 A 40-nm, 2M-Cell, 8b-Precision, Hybrid SLC-MLC PCM Computing-in-Memory Macro with 20.5 - 65.0TOPS/W for Tiny-AI Edge Devices
22.11.4 An 8-Mb DC-Current-Free Binary-to-8b Precision ReRAM Nonvolatile Computing-in-Memory Macro using Time-Space-Readout with 1286.4 - 21.6TOPS/W for Edge-AI Devices
22.11.8 A 28nm 1Mb Time-Domain Computing-in-Memory 6T-SRAM Macro with a 6.6ns Latency, 1241GOPS and 37.01TOPS/W for 8b-MAC Operations for Edge-AI Devices
22.15.3 COMB-MCM: Computing-on-Memory-Boundary NN Processor with Bipolar Bitwise Sparsity Optimization for Scalable Multi-Chiplet-Module Edge Machine Learning
22.15.6 DIANA: An End-to-End Energy-Efficient DIgital and ANAlog Hybrid Neural Network SoC
数字式
21.16.4 An 89TOPS/W and 16.3TOPS/mm2 All-Digital SRAM-Based Full-Precision Compute-In Memory Macro in 22nm for Machine-Learning Edge Applications
22.11.1 A 1ynm 1.25V 8Gb, 16Gb/s/pin GDDR6-based Accelerator-in-Memory supporting 1TFLOPS MAC Operation and Various Activation Functions for Deep-Learning Applications
22.11.6 A 5-nm 254-TOPS/W 221-TOPS/mm2 Fully-Digital Computing-in-Memory Macro Supporting Wide-Range Dynamic-Voltage-Frequency Scaling and Simultaneous MAC and Write Operations
22.11.7 A 1.041-Mb/mm2 27.38-TOPS/W Signed-INT8 Dynamic-Logic-Based ADC-less SRAM Compute-In-Memory Macro in 28nm with Reconfigurable Bitwise Operation for AI and Embedded Applications
22.15.5 A 28nm 29.2TFLOPS/W BF16 and 36.5TOPS/W INT8 Reconfigurable Digital CIM Processor with Unified FP/INT Pipeline and Bitwise In-Memory Booth Multiplication for Cloud Deep Learning Acceleration
22.29.1 184QPS/W 64Mb/mm2 3D Logic-to-DRAM Hybrid Bonding with Process-Near-Memory Engine for Recommendation System
22.29.3 A 28nm 15.59μJ/Token Full-Digital Bitline-Transpose CIM-Based Sparse Transformer Accelerator with Pipeline/Parallel Reconfigurable Modes
标签：Computing,AI,近存,2023,TOPS,存内,Edge,Memory,Macro From： https://www.cnblogs.com/sasasatori/p/17020890.html