[HTML][HTML] Prospects and challenges of electrochemical random-access memory for deep-learning accelerators
The ever-expanding capabilities of machine learning are powered by exponentially growing
complexity of deep neural network (DNN) models, requiring more energy and chip-area …
complexity of deep neural network (DNN) models, requiring more energy and chip-area …
Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing
Processing information in the optical domain promises advantages in both speed and
energy efficiency over existing digital hardware for a variety of emerging applications in …
energy efficiency over existing digital hardware for a variety of emerging applications in …
A Heterogeneous Chiplet Architecture for Accelerating End-to-End Transformer Models
Transformers have revolutionized deep learning and generative modeling, enabling
unprecedented advancements in natural language processing tasks. However, the size of …
unprecedented advancements in natural language processing tasks. However, the size of …
Data Pruning-enabled High Performance and Reliable Graph Neural Network Training on ReRAM-based Processing-in-Memory Accelerators
Graph Neural Networks (GNNs) have achieved remarkable accuracy in cognitive tasks such
as predictive analytics on graph-structured data. Hence, they have become very popular in …
as predictive analytics on graph-structured data. Hence, they have become very popular in …
Experimental demonstration of non-stateful in-memory logic with 1t1r oxram valence change mechanism memristors
H Padberg, A Regev, G Piccolboni… - … on Circuits and …, 2023 - ieeexplore.ieee.org
Processing-in-memory (PIM) is attractive to overcome the limitations of modern computing
systems. Numerous PIM systems exist, varying by the technologies and logic techniques …
systems. Numerous PIM systems exist, varying by the technologies and logic techniques …
DRCTL: A Disorder-Resistant Computation Translation Layer Enhancing the Lifetime and Performance of Memristive CIM Architecture
The memristive Computing-in-Memory (CIM) sys-tem can efficiently accelerate matrix-vector
multiplication (MVM) operations through in-situ computing. The data layout has a significant …
multiplication (MVM) operations through in-situ computing. The data layout has a significant …
HpT: Hybrid Acceleration of Spatio-Temporal Attention Model Training on Heterogeneous Manycore Architectures
Transformer models have become widely popular in numerous applications, and especially
for building foundation large language models (LLMs). Recently, there has been a surge in …
for building foundation large language models (LLMs). Recently, there has been a surge in …
ARAS: An Adaptive Low-Cost ReRAM-Based Accelerator for DNNs
Processing Using Memory (PUM) accelerators have the potential to perform Deep Neural
Network (DNN) inference by using arrays of memory cells as computation engines. Among …
Network (DNN) inference by using arrays of memory cells as computation engines. Among …
Efficient Reprogramming of Memristive Crossbars for DNNs: Weight Sorting and Bit Stucking
We introduce a novel approach to reduce the number of times required for reprogramming
memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs) …
memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs) …
Crafting Non-Volatile Memory (NVM) Hierarchies: Optimizing Performance, Reliability, and Energy Efficiency
Crafting Non-Volatile Memory (NVM) Hierarchies: Optimizing Performance, Reliability, and
Energy Efficiency / Carlos Escuín Blas Page 1 2024 228 Carlos Escuín Blasco Crafting Non-Volatile …
Energy Efficiency / Carlos Escuín Blas Page 1 2024 228 Carlos Escuín Blasco Crafting Non-Volatile …