32 citations · 134 across the 34 of their papers we have counts for
39 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…
Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition
Tauseef Ahmed, Tao Sun, Jeronimo Castrillon +2
Deep learning has greatly advanced automatic speech recognition (ASR), enabling widespread deployment on edge devices such as smartphones and smart home systems. However, the compu…
Demonstrating a Future for MLIR-native DSL Compilers on a NumPy-like Example
Karl F. A. Friebel, Jascha A. Ohlmann, Jeronimo Castrillon
Compilers for general-purpose languages have been shown to be at a disadvantage when it comes to specialized application domains as opposed to their Domain-Specific Language (DSL)…
Interferences within a certifiable design methodology for high-performance multi-core platforms
Mohamed Amine Khelassi, Felix Suchert, Abderaouf Amalou +8
The adoption of high-performance multi-core platforms in avionics and automotive systems introduces significant challenges in ensuring predictable execution, primarily due to share…
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…
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…
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,…
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…
PretVM: Predictable, Efficient Virtual Machine for Real-Time Concurrency
Shaokai Lin, Erling Jellum, Mirco Theile +10
This paper introduces the Precision-Timed Virtual Machine (PretVM), an intermediate platform facilitating the execution of quasi-static schedules compiled from a subset of programs…
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…
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…
HElium: A Language and Compiler for Fully Homomorphic Encryption with Support for Proxy Re-Encryption
Mirko Günther, Lars Schütze, Kilian Becher +2
Privacy-preserving analysis of confidential data can increase the value of such data and even improve peoples' lives. Fully homomorphic encryption (FHE) can enable privacy-preservi…
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…
High-Performance Deterministic Concurrency using Lingua Franca
Christian Menard, Marten Lohstroh, Soroush Bateni +10
Actor frameworks and similar reactive programming techniques are widely used for building concurrent systems. They promise to be efficient and scale well to a large number of cores…
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…
mAPN: Modeling, Analysis, and Exploration of Algorithmic and Parallelism Adaptivity
Hasna Bouraoui, Chadlia Jerad, Omar Romdhani +1
Using parallel embedded systems these days is increasing. They are getting more complex due to integrating multiple functionalities in one application or running numerous ones conc…
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…
Automatic Creation of High-Bandwidth Memory Architectures from Domain-Specific Languages: The Case of Computational Fluid Dynamics
Stephanie Soldavini, Karl F. A. Friebel, Mattia Tibaldi +3
Numerical simulations can help solve complex problems. Most of these algorithms are massively parallel and thus good candidates for FPGA acceleration thanks to spatial parallelism.…