A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog IMC (IBM and ETH Zurich)


A technical paper titled “A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing” was published by researchers at IBM Research Europe and IIS-ETH Zurich. Abstract: "Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is requ... » read more

Analog In-Memory Cores With Multi-Memristive Unit-Cells (IBM)


A technical paper titled “Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing” was published by researchers at IBM Research-Europe, IBM Research-Albany, and IBM Research-Yorktown Heights. Abstract: "Analog in-memory computing (AIMC) using memristive devices is considered a promising Non-von Neumann approach for deep learning (DL... » read more

Comparing Analog and Digital SRAM In-Memory Computing Architectures (KU Leuven)


A technical paper titled "Benchmarking and modeling of analog and digital SRAM in-memory computing architectures" was published by researchers at KU Leuven. Abstract: "In-memory-computing is emerging as an efficient hardware paradigm for deep neural network accelerators at the edge, enabling to break the memory wall and exploit massive computational parallelism. Two design models have surge... » read more

Analog Edge Inference with ReRAM


Abstract "As the demands of big data applications and deep learning continue to rise, the industry is increasingly looking to artificial intelligence (AI) accelerators. Analog in-memory computing (AiMC) with emerging nonvolatile devices enable good hardware solutions, due to its high energy efficiency in accelerating the multiply-and-accumulation (MAC) operation. Herein, an Applied Materials... » read more