I am Fabrizio M. Aymone, an Electronics Engineer and Researcher with a strong passion for AI, High-Performance Computing, and their application in quantitative fields. I recently graduated with honors from Politecnico di Milano and am pursuing an MSc in Electronic Engineering at ETH Zurich.
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ETH Zurich
- Zurich, Switzerland
- https://fabrizioaymone.github.io/
- https://orcid.org/0009-0002-2188-1436
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suitability-of-Forward-Forward-and-PEPITA-learning
suitability-of-Forward-Forward-and-PEPITA-learning PublicThis repository contains the spreadsheet of the quantitative analysis performed for the paper "Suitability of Forward-Forward and PEPITA Learning to MLCommons-Tiny benchmarks".
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forward-learning-of-LLMs-to-consumer-devices
forward-learning-of-LLMs-to-consumer-devices PublicRepo for the paper "Forward Learning of Large Language Models by Consumer Devices". Includes code for BP, PEPITA, and MEMPEPITA algorithms on Transformer models, and analyses for memory, complexity…
Python 1
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cuda-smith-waterman
cuda-smith-waterman PublicRepository containing the implementation in CUDA for the Smith-Waterman algorithm for the GPU101 PiA Course Project
Cuda
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riscv-tflite
riscv-tflite PublicThis repo evaluates TensorFlow Lite for Microcontrollers (TFLite Micro) on RISC-V architectures, featuring cross-compilation and performance profiling with Spike and Gem5. It provides insights into…
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merge-sort-fpga
merge-sort-fpga PublicRepository containing the design of an IP block for Merge Sort acceleration on FPGA for the FPGA101 PiA Course Project
VHDL 1
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DeepBeliefNetwork
DeepBeliefNetwork PublicC implementation of the Deep Belief Network for classifying MNIST digits discussed in G. Hinton paper "A fast learning algorithm for deep belief nets".
C
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