A customizable hardware prefetching framework using online reinforcement learning as described in the MICRO 2021 paper by Bera et al. (https://arxiv.org/pdf/2109.12021.pdf).

Using Belady's algorithm for improved cache replacement

A project for Advanced Operating System(CS604) that implements ARC cache replacement policy.

The PyTorch codebase for -> DEAP Cache: Deep Eviction Admission and Prefetching for Cache.

Trace-driven cache memory simulator with LRU, MRU, RR and Belady replacement policies.

Cache Replacement Championship - 2

High performing caching package for node/javascript

Implemented modern last-level cache(LLC) with the concept of "Perceptron Learning for Reuse Prediction" that use neural network idea, which is training the predictor by a smaller independent cache with a series of features.

Source code for the cache replacement policy published in [Faldu et al., PACT'17] and [Faldu et al., CRC2'17].

Source code for the cache replacement policy published in [Faldu et al., PACT'17] and [Faldu et al., CRC2'17].

A transformer-based cache replacement model for CDNs using imitation learning technique with Belady's optimal policy

Programs implemented during lab sessions of course CSC403 i.e (Computer Organization & Architecture)

A console application to simulate cache and cache replacement policies : LRU, LFU and MRU

Implementation of Cache Replacement Algorithm (FIFO and LRU) and calculation of corresponding Cache Hit Ratio.

Reproduces the results of the FAST 2021 paper, Learning Cache Replacement with CACHEUS, for Academic purposes.

This repository contains a set-associative cache simulator in C with four eviction policies: LRU, LFU, Bélády's OPT, and an online perceptron-based policy that scores by recency, frequency, and per-PC weights updated from a circular eviction history to explore whether a lightweight ML model can approximate OPT at runtime with no offline training.

Cache replacement algorithms, including Least Recently Used (LRU), First In First Out (FIFO), and Enhanced Not Recently Used (ENRU).

Simulates and compares FIFO, LRU, XGBoost, LightGBM, Simulates and compares FIFO, LRU, XGBoost, LightGBM, and Actor‑Critic RL cache policies on Zipfian traces, reporting hit rates against an optimal baseline.

ARC Cache Implementation (in Haskell!)