1 citations · 1 across the 6 of their papers we have counts for
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Interpolation of Non-Linear Functions for LLMs using Partial Reconfiguration in FPGAs
Roger Morales-Monge, Nazareth Jimenez-Chacon, Jose Gabriel Villalobos-Alvarado +2
Non-linear functions such as exponential and sigmoid are essential in AI and LLM acceleration, although implementing them efficiently on FPGAs is still costly. This paper proposes…
CAMTA: A Reconfigurable Multi-Region Activation Unit for Nonlinear Function Approximation
Carlos Soto-Porras, Jose Fonseca-Cruz, Pablo Ramirez-Morera +3
Nonlinear activation functions are widely used in machine learning workloads, but their direct hardware implementation is often costly, function-specific, or difficult to reuse acr…
A Quantitative Evaluation of Approximate Softmax Functions for Deep Neural Networks
Anthony Leiva-Valverde, Fabricio Elizondo-Fernández, Luis G. León-Vega +2
The softmax function is a widely used activation function in the output layers of neural networks, responsible for converting raw scores into class probabilities while introducing…