9 citations · 29 across the 10 of their papers we have counts for
12 papers
A gem5-based Simulation Framework for Computing-in-DRAM
Alexander Kusnezoff, João Paulo C. de Lima, Jeronimo Castrillon +1
Computing-in-Memory using DRAM (CIMD) has demonstrated substantial energy and throughput gains for memory-bound workloads consisting of bulk-bitwise operations, by performing compu…
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…
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…
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…
Towards Error Correction for Computing in Racetrack Memory
Preston Brazzle, Benjamin F. Morris, Evan McKinney +4
Computing-in-memory (CIM) promises to alleviate the Von Neumann bottleneck and accelerate data-intensive applications. Depending on the underlying technology and configuration, CIM…
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…
Full-Stack Optimization for CAM-Only DNN Inference
João Paulo C. de Lima, Asif Ali Khan, Luigi Carro +1
The accuracy of neural networks has greatly improved across various domains over the past years. Their ever-increasing complexity, however, leads to prohibitively high energy deman…
C4CAM: A Compiler for CAM-based In-memory Accelerators
Hamid Farzaneh, João Paulo Cardoso de Lima, Mengyuan Li +3
Machine learning and data analytics applications increasingly suffer from the high latency and energy consumption of conventional von Neumann architectures. Recently, several in-me…
CINM (Cinnamon): A Compilation Infrastructure for Heterogeneous Compute In-Memory and Compute Near-Memory Paradigms
Asif Ali Khan, Hamid Farzaneh, Karl F. A. Friebel +3
The rise of data-intensive applications exposed the limitations of conventional processor-centric von-Neumann architectures that struggle to meet the off-chip memory bandwidth dema…
DNA Pre-alignment Filter using Processing Near Racetrack Memory
Fazal Hameed, Asif Ali Khan, Sebastien Ollivier +2
Recent DNA pre-alignment filter designs employ DRAM for storing the reference genome and its associated meta-data. However, DRAM incurs increasingly high energy consumption backgro…
Brain-inspired Cognition in Next Generation Racetrack Memories
Asif Ali Khan, Sebastien Ollivier, Stephen Longofono +3
Hyperdimensional computing (HDC) is an emerging computational framework inspired by the brain that operates on vectors with thousands of dimensions to emulate cognition. Unlike con…