32 citations · 134 across the 34 of their papers we have counts for
5 papers · 1 filter
Efficient In-Memory Acceleration of Sparse Block Diagonal LLMs
João Paulo Cardoso de Lima, Marc Dietrich, Jeronimo Castrillon +1
Structured sparsity enables deploying large language models (LLMs) on resource-constrained systems. Approaches like dense-to-sparse fine-tuning are particularly compelling, achievi…
CoMoNM: A Cost Modeling Framework for Compute-Near-Memory Systems
Hamid Farzaneh, Asif Ali Khan, Jeronimo Castrillon
Compute-Near-Memory (CNM) systems offer a promising approach to mitigate the von Neumann bottleneck by bringing computational units closer to data. However, optimizing for these ar…
Leveraging Stochastic Depth Training for Adaptive Inference
Guilherme Korol, Antonio Carlos Schneider Beck, Jeronimo Castrillon
Dynamic DNN optimization techniques such as layer-skipping offer increased adaptability and efficiency gains but can lead to i) a larger memory footprint as in decision gates, ii)…
All-in-Memory Stochastic Computing using ReRAM
João Paulo C. de Lima, Mehran Shoushtari Moghadam, Sercan Aygun +3
As the demand for efficient, low-power computing in embedded and edge devices grows, traditional computing methods are becoming less effective for handling complex tasks. Stochasti…
Modeling and Simulating Emerging Memory Technologies: A Tutorial
Yun-Chih Chen, Tristan Seidl, Nils Hölscher +15
Non-volatile Memory (NVM) technologies present a promising alternative to traditional volatile memories such as SRAM and DRAM. Due to the limited availability of real NVM devices,…