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cs.LG2026
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…
cs.LG2025
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…