16 citations · 34 across the 18 of their papers we have counts for
9 papers · 1 filter
MING: An Automated CNN-to-Edge MLIR HLS framework
Jiahong Bi, Lars Schütze, Jeronimo Castrillon
Driven by the increasing demand for low-latency and real-time processing, machine learning applications are steadily migrating toward edge computing platforms, where Field-Programm…
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…
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,…
Count2Multiply: Reliable In-Memory High-Radix Counting
João Paulo Cardoso de Lima, Benjamin Franklin Morris, Asif Ali Khan +2
Computing-in-memory (CIM) has been demonstrated across various memory technologies, ranging from memristive crossbars performing analog dot-product computations to large-scale digi…
A System Development Kit for Big Data Applications on FPGA-based Clusters: The EVEREST Approach
Christian Pilato, Subhadeep Banik, Jakub Beranek +28
Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately sup…
The Landscape of Compute-near-memory and Compute-in-memory: A Research and Commercial Overview
Asif Ali Khan, João Paulo C. De Lima, Hamid Farzaneh +1
In today's data-centric world, where data fuels numerous application domains, with machine learning at the forefront, handling the enormous volume of data efficiently in terms of t…