3 papers
cs.AR2025
Hardware-Software Co-Design for Accelerating Transformer Inference Leveraging Compute-in-Memory
Dong Eun Kim, Tanvi Sharma, Kaushik Roy
Transformers have become the backbone of neural network architecture for most machine learning applications. Their widespread use has resulted in multiple efforts on accelerating a…
cs.ET2024
Approximate ADCs for In-Memory Computing
Arkapravo Ghosh, Hemkar Reddy Sadana, Mukut Debnath +5
In memory computing (IMC) architectures for deep learning (DL) accelerators leverage energy-efficient and highly parallel matrix vector multiplication (MVM) operations, implemented…
cs.ET2024
WAGONN: Weight Bit Agglomeration in Crossbar Arrays for Reduced Impact of Interconnect Resistance on DNN Inference Accuracy
Jeffry Victor, Dong Eun Kim, Chunguang Wang +2
Deep neural network (DNN) accelerators employing crossbar arrays capable of in-memory computing (IMC) are highly promising for neural computing platforms. However, in deeply scaled…