8 citations · 11 across the 4 of their papers we have counts for
6 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…
RAPID: Row-Parallel Arithmetic Processing in DRAM
William C. Tegge, João Paulo Cardoso de Lima, Shouzhi Fang +2
Processing-using-memory (PUM) architectures perform computation directly within DRAM to reduce costly data movement between memory and processors. Because charge-sharing operations…
Compiler-Assisted Speculative Sampling for Accelerated LLM Inference on Heterogeneous Edge Devices
Alejandro Ruiz y Mesa, Guilherme Korol, Moritz Riesterer +2
LLM deployment on resource-constrained edge devices faces severe latency constraints, particularly in real-time applications where delayed responses can compromise safety or usabil…
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…