8 papers
Comfortably Stuck: Evaluation Pitfalls in Bench2Drive's Closed-Loop Protocol
Thomas Schwarzfischer, Emad Aghajanzadeh, Moritz Thoma +5
Closed-loop evaluation is widely regarded as a more faithful way to assess end-to-end (E2E) driving models than open-loop evaluation, and Bench2Drive, built on the CARLA simulator,…
MemFLoRA: Memory-Floor LoRA for CNN Adaptation at the Edge
Mehmet Emre Akbulut, Johannes Geier, Ulf Schlichtmann
On-device learning is necessary when the model encounters user-,sensor-, or environment-specific shifts after deployment. Although parameter-efficient fine-tuning (PEFT) methods, p…
Mind the Approximation: Fisher-Weighted SVD Compression for ViTs
Moritz Thoma, Maximilian Groezinger, Maximilian Forstenhäusler +7
Model compression is key to mitigate deployment challenges of ever growing machine learning models. In this area of research, singular value decomposition (SVD)-based compression o…
A Systolic Array Architecture for Nonlinear Activation Functions and Softmax Computation using Chebyshev Polynomials
Benedikt Schaible, Anirudh Suresh Bharadwaj, Ulf Schlichtmann +1
Neural Network Accelerators have gained popularity in recent years due to their greater efficiency than CPU-based platforms. Often, these accelerators utilize different hardware un…
KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a…
OptINC: Optical In-Network-Computing for Scalable Distributed Learning
Sijie Fei, Grace Li Zhang, Bing Li +1
Distributed learning is widely used for training large models on large datasets by distributing parts of the model or dataset across multiple devices and aggregating the computed r…
Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification
Mikhael Djajapermana, Moritz Reiber, Daniel Mueller-Gritschneder +1
Hybrids of Convolutional Neural Network (CNN) and Vision Transformer (ViT) have outperformed pure CNN or ViT architecture. However, since these architectures require large paramete…
A High-Level Compiler Integration Approach for Deep Learning Accelerators Supporting Abstraction and Optimization
Samira Ahmadifarsani, Daniel Mueller-Gritschneder, Ulf Schlichtmann
The growing adoption of domain-specific architectures in edge computing platforms for deep learning has highlighted the efficiency of hardware accelerators. However, integrating cu…