Amir-HK's repositories
ramulator-pim
A fast and flexible simulation infrastructure for exploring general-purpose processing-in-memory (PIM) architectures. Ramulator-PIM combines a widely-used simulator for out-of-order and in-order processors (ZSim) with Ramulator, a DRAM simulator with memory models for DDRx, LPDDRx, GDDRx, WIOx, HBMx, and HMCx. Ramulator is described in the IEEE CAL 2015 paper by Kim et al. at https://people.inf.ethz.ch/omutlu/pub/ramulator_dram_simulator-ieee-cal15.pdf Ramulator-PIM is used in the DAC 2019 paper by Singh et al. at https://people.inf.ethz.ch/omutlu/pub/NAPEL-near-memory-computing-performance-prediction-via-ML_dac19.pdf
BACS
Benchmarks for Approximate Circuit Synthesis
Binary-Neural-Networks
Implemented here a Binary Neural Network (BNN) achieving nearly state-of-art results but recorded a significant reduction in memory usage and total time taken during training the network.
DAMOV
DAMOV is a benchmark suite and a methodical framework targeting the study of data movement bottlenecks in modern applications. It is intended to study new architectures, such as near-data processing. Described by Oliveira et al. (preliminary version at https://arxiv.org/pdf/2105.03725.pdf)
DNN_NeuroSim_V2.1
Benchmark framework of compute-in-memory based accelerators for deep neural network (on-chip training chip focused)
Eva-CiM
Code of "Eva-CiM: A System-Level Performance and Energy Evaluation Framework for Computing-in-Memory Architectures", TCAD 2020
IMAC
IMAC is an In-memory Multiply and ACcumulation Engine (TCAS 2020)
machine-learning-cheat-sheet
Classical equations and diagrams in machine learning
mcpat
An integrated power, area, and timing modeling framework for multicore and manycore architectures
MemTorch
A Simulation Framework for Memristive Deep Learning Systems
MLP_NeuroSim_V3.0
Benchmark framework of synaptic device technologies for a simple neural network
MNSIM-2.0
A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems
MultiPIM
MultiPIM: A Detailed and Configurable Multi-Stack Processing-In-Memory Simulator
prim-benchmarks
PrIM (Processing-In-Memory benchmarks) is the first benchmark suite for a real-world processing-in-memory (PIM) architecture. PrIM is developed to evaluate, analyze, and characterize the first publicly-available real-world PIM architecture, the UPMEM PIM architecture. Described by Gómez-Luna et al. (preliminary version at https://arxiv.org/abs/2105.03814).
pytorch-lightning
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
ramulator
A Fast and Extensible DRAM Simulator, with built-in support for modeling many different DRAM technologies including DDRx, LPDDRx, GDDRx, WIOx, HBMx, and various academic proposals. Described in the IEEE CAL 2015 paper by Kim et al. at http://users.ece.cmu.edu/~omutlu/pub/ramulator_dram_simulator-ieee-cal15.pdf
SC-DNN
Stochastic Computing for Deep Neural Networks
scsynth
Synthesis tool for stochastic computing
SIMPLE-MAGIC
SIMPLE MAGIC: Synthesis and In-memory MaPping of Logic Execution for Memristor Aided loGIC
simple-neural-network
A simple Python script showing how the backpropagation algorithm works.
TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
tensorflow_2_tutorials
Tensorflow 2.0 tutorials
tf-approximate
Approximate layers - TensorFlow extension
zsim
A fast and scalable x86-64 multicore simulator