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IMComputing - summaries and pictures

2020 Bavikadi A Review of In-Memory Computing Architectures for Machine Learning Applications

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  • architectures

DRAM stores data capacitively which requires frequent refreshing, making the logic operations timing sensitive.

SRAM is limited by the comparatively lower cell density and energy efficiency.

ReRAM suffers from high write latency and

STT-MRAM suffers from high error rate due to fluctuations in ohmic properties of the cells

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Magnetic memories (STT-MRAM/SOT-MRAM), especially multi-level cell MRAMs (MLC-STT) can be expected to achieve more popularity as IMC platform due to their non-volatility as well as a wider functionality range compared to DRAM and SRAM

2019 Bing Li An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications

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  • all processors attempted before 2020

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2020 Amirali Amirsoleimani In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives

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  • spider diagrams

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This kind of binary memory could combine high switching speed (sub-ns), low energy (pJ range), and high endur-ance ($10^{12}$ cycles) of DRAM and SRAM with non- volatility (>10 years retention) and scalability (<10 nm)