Publications (4)
Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks
Muhammad Sabih, Frank Hannig, Juergen Teich
For many applications, utilizing DNNs (Deep Neural Networks) requires their implementation on a target architecture in an optimized manner concerning energy consumption, memory req…
Hardware/Software Co-Design of RISC-V Extensions for Accelerating Sparse DNNs on FPGAs
Muhammad Sabih, Abrarul Karim, Jakob Wittmann +2
The customizability of RISC-V makes it an attractive choice for accelerating deep neural networks (DNNs). It can be achieved through instruction set extensions and corresponding cu…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Rethinking Neural Nonlinearity as Gating
Muhammad Sabih, Frank Hannig, Jürgen Teich
Activation functions are considered an essential primitive for neural nonlinearity, i.e., they enable neural networks to serve as universal approximators. In this paper, we show th…