A full spectrum of computing-in-memory technologies
Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …
provide sustainable improvements in computing throughput and energy efficiency …
Memristor-based hardware accelerators for artificial intelligence
Satisfying the rapid evolution of artificial intelligence (AI) algorithms requires exponential
growth in computing resources, which, in turn, presents huge challenges for deploying AI …
growth in computing resources, which, in turn, presents huge challenges for deploying AI …
An Energy-Efficient Computing-in-Memory NN Processor With Set-Associate Blockwise Sparsity and Ping-Pong Weight Update
Computing-in-memory (CIM) chips have demonstrated the potential high energy efficiency
for low-power neural network (NN) processors. Even with energy-efficient CIM macros, the …
for low-power neural network (NN) processors. Even with energy-efficient CIM macros, the …
A Multiply-Less Approximate SRAM Compute-In-Memory Macro for Neural-Network Inference
Compute-in-memory (CIM) is promising in reducing data movement energy and providing
large bandwidth for matrix-vector multiplies (MVMs). However, existing work still faces …
large bandwidth for matrix-vector multiplies (MVMs). However, existing work still faces …
Efficient Processing of MLPerf Mobile Workloads Using Digital Compute-In-Memory Macros
Compute-in-memory (CIM) has recently emerged as a promising design paradigm to
accelerate deep neural network (DNN) processing. Continuously better energy and area …
accelerate deep neural network (DNN) processing. Continuously better energy and area …
A Multi-Chiplet Computing-in-Memory Architecture Exploration Framework Based on Various CIM Devices
Z Dai, F Xiang, X Fu, Y He, W Sun, Y Liu… - … on Computer-Aided …, 2024 - ieeexplore.ieee.org
Computing-in-memory (CIM) architectures based on various devices, such as resistive
random access memory, SRAM, DRAM, etc., have demonstrated promising energy …
random access memory, SRAM, DRAM, etc., have demonstrated promising energy …
Analog or Digital In-memory Computing? Benchmarking through Quantitative Modeling
In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient,
throughput-efficient and area-efficient machine learning at the edge. However, the …
throughput-efficient and area-efficient machine learning at the edge. However, the …
A 28-nm Computing-in-Memory-Based Super-Resolution Accelerator Incorporating Macro-Level Pipeline and Texture/Algebraic Sparsity
Super-resolution (SR) task using the convolutional neural network is a crucial task in
improving image and video quality. The introduction of the residual block (RB) raises the …
improving image and video quality. The introduction of the residual block (RB) raises the …
DDC-PIM: Efficient Algorithm/Architecture Co-Design for Doubling Data Capacity of SRAM-Based Processing-in-Memory
Processing-in-memory (PIM), as a novel computing paradigm, provides significant
performance benefits from the aspect of effective data movement reduction. SRAM-based …
performance benefits from the aspect of effective data movement reduction. SRAM-based …
20.2 A 28nm 74.34 TFLOPS/W BF16 Heterogenous CIM-Based Accelerator Exploiting Denoising-Similarity for Diffusion Models
R Guo, L Wang, X Chen, H Sun, Z Yue… - … Solid-State Circuits …, 2024 - ieeexplore.ieee.org
Diffusion models (DMs) have emerged as a powerful category of generative models with
record-breaking performance in image synthesis 1. A noisy image created from pure …
record-breaking performance in image synthesis 1. A noisy image created from pure …