11 papers
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)…
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…
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…
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…