Peat, a Python-based Intel-Optimized Tensorflow dockerization with CPU & Memory constraints configurator
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Updated
Aug 5, 2020 - Python
Peat, a Python-based Intel-Optimized Tensorflow dockerization with CPU & Memory constraints configurator
Hardware Accelerated QNN for Arrhythmia classification on Edge devices
Real-time stereoscopic depth mapping on dual Zedboards with a Go WebUI and hardware-accelerated SAD disparity.
High-performance FAST Corner Detector on PYNQ with using Hardware Acceleration (80+ FPS).
A VHDL implementation of the Sobel edge detection algorithm featuring a 3x3 windowing pipeline with line buffers written for xc7a100tcsg324-1 in Vivado 2025. Also includes a simulation benchmark.
ANANA (Asistente Normalizador Audiovisual Nada Abrupto)
Curiosity killed the ignorant cat
FPGA-accelerated matrix multiplication engine for running Large Language Models (LLMs) on local hardware, demonstrating substantial speed-ups and energy savings.
show rtsp stream in streamlit web via ffmpeg, You can set the hardware acceleration you support in the ffmpeg command.
😱 RoCC Accelerator Integration with Chipyard
FPGA-based MNIST CNN Accelerator achieving 3339x speedup vs MicroBlaze soft-core. (HW/SW Co-design on Urbana Board)
FPGA-based acceleration of TinyLLaVA-Phi-2-SigLIP-3.1B inference on AMD Alveo U280 using Vitis HLS.
Progetto universitario. Realizzazione in VHDL, test e sintesi con Xilinx Vivado per FPGA di un CIC filter.
Learned Approximate Matrix Profile (LAMP) implementation on Ultra96-v2 board
HW Architecture-Mapping Design Space Exploration Framework for Deep Learning Accelerators
Hashslayer is an open-source demo tool showcasing AWS EC2 F1 FPGA instances' power in cracking hashes. It leverages custom hardware for accelerated cryptographic operations, highlighting innovative FPGA-based performance.
A hardware implementation of a deep learning accelerator using SystemVerilog/Verilog, designed for efficient neural network inference. This project implements a systolic array-based matrix multiplication unit with various activation functions and supporting components.
A really fast, secure random file generator. Much faster than /dev/urandom.
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