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TransPIM: A memory-based acceleration via software-hardware co-design for transformer
Transformer-based models are state-of-the-art for many machine learning (ML) tasks.
Executing Transformer usually requires a long execution time due to the large memory …
Executing Transformer usually requires a long execution time due to the large memory …
HARDSEA: Hybrid analog-ReRAM clustering and digital-SRAM in-memory computing accelerator for dynamic sparse self-attention in transformer
Self-attention-based transformers have outperformed recurrent and convolutional neural
networks (RNN/CNNs) in many applications. Despite the effectiveness, calculating self …
networks (RNN/CNNs) in many applications. Despite the effectiveness, calculating self …
P3 ViT: A CIM-Based High-Utilization Architecture With Dynamic Pruning and Two-Way **-Pong Macro for Vision Transformer
X Fu, Q Ren, H Wu, F ** and pipeline optimization
The pipeline is an efficient solution to boost performance in non-volatile memory based
computing in memory (nvCIM) convolution neural network (CNN) accelerators. However, the …
computing in memory (nvCIM) convolution neural network (CNN) accelerators. However, the …
E-MAC: Enhanced In-SRAM MAC Accuracy via Digital-to-Time Modulation
In this article, we introduce a novel technique called E-multiplication and accumulation
(MAC)(EMAC), aimed at enhancing energy efficiency, reducing latency, and improving the …
(MAC)(EMAC), aimed at enhancing energy efficiency, reducing latency, and improving the …
Allspark: Workload Orchestration for Visual Transformers on Processing In-Memory Systems
The advent of Transformers has revolutionized computer vision, offering a powerful
alternative to convolutional neural networks (CNNs), especially with the local attention …
alternative to convolutional neural networks (CNNs), especially with the local attention …
[КНИГА][B] Software-Hardware Co-design for Processing In-Memory Accelerators
M Zhou - 2023 - search.proquest.com
The explosive increase in data volume in emerging applications poses grand challenges to
computing systems because the bandwidth between compute and memory cannot keep up …
computing systems because the bandwidth between compute and memory cannot keep up …
Accelerating Neural Network Training with Processing-in-Memory GPU
Processing-in-memory (PIM) architecture is promising for accelerating deep neural network
(DNN) training due to its low-latency and energy-efficient data movement between …
(DNN) training due to its low-latency and energy-efficient data movement between …