Pinned Repositories
A-Growing-List-of-Papers-on-Stochastic-Computing
AIChip_Paper_List
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
gittutorial
PIMSim
PIMSim is a Process-In-Memory Simulator with the compatibility of GEM5 full-system simulation.
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
Amir-HK's Repositories
Amir-HK/gittutorial
Amir-HK/PIMSim
PIMSim is a Process-In-Memory Simulator with the compatibility of GEM5 full-system simulation.
Amir-HK/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
Amir-HK/A-Growing-List-of-Papers-on-Stochastic-Computing
Amir-HK/AIChip_Paper_List
Amir-HK/BACS
Benchmarks for Approximate Circuit Synthesis
Amir-HK/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.
Amir-HK/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)
Amir-HK/DNN_NeuroSim_V2.1
Benchmark framework of compute-in-memory based accelerators for deep neural network (on-chip training chip focused)
Amir-HK/Eva-CiM
Code of "Eva-CiM: A System-Level Performance and Energy Evaluation Framework for Computing-in-Memory Architectures", TCAD 2020
Amir-HK/IMAC
IMAC is an In-memory Multiply and ACcumulation Engine (TCAS 2020)
Amir-HK/machine-learning-cheat-sheet
Classical equations and diagrams in machine learning
Amir-HK/mcpat
An integrated power, area, and timing modeling framework for multicore and manycore architectures
Amir-HK/MemTorch
A Simulation Framework for Memristive Deep Learning Systems
Amir-HK/MLP_NeuroSim_V3.0
Benchmark framework of synaptic device technologies for a simple neural network
Amir-HK/MNSIM-2.0
A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems
Amir-HK/MultiPIM
MultiPIM: A Detailed and Configurable Multi-Stack Processing-In-Memory Simulator
Amir-HK/PIMProf
Amir-HK/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).
Amir-HK/pytorch-lightning
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
Amir-HK/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
Amir-HK/SC-DNN
Stochastic Computing for Deep Neural Networks
Amir-HK/scsynth
Synthesis tool for stochastic computing
Amir-HK/SIMPLE-MAGIC
SIMPLE MAGIC: Synthesis and In-memory MaPping of Logic Execution for Memristor Aided loGIC
Amir-HK/simple-neural-network
A simple Python script showing how the backpropagation algorithm works.
Amir-HK/Stochastic-computing-based-neural-network-accelerator
Amir-HK/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Amir-HK/tensorflow_2_tutorials
Tensorflow 2.0 tutorials
Amir-HK/tf-approximate
Approximate layers - TensorFlow extension
Amir-HK/zsim
A fast and scalable x86-64 multicore simulator