in-memory-computing

Apache Ignite

Apache Ignite 3

A secure, high-performance, multi-tenant micro-service platform for remote "App Management" and "Computing"

[ASPLOS 2019] PUMA-simulator provides a detailed simulation model of a dataflow architecture built with NVM (non-volatile memory), and runs ML models compiled using the puma compiler.

[Nature Machine Intelligence 2023] "Echo state graph neural networks with analogue random resistive memory arrays."

Software side of an LLM running inside a DRAM chip via charge-sharing PIM (BitNet b1.58 on DRAM-Bender silicon).

Apache Ignite Website

The DaiSy Library for Fast and Exact, Data Series and Vector Similarity Search

A computational foundation for AGI based on hyperdimensional computing and set theory.

Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey. By designing and analyzing Memristor Devices for Neuromorphic Computing, Spiking Neural Networks (SNNs), Physically Unclonable Functions (PUFs), True Random Number Generators (TRNGs), we are investigating their hardware and software security (attacks and defenses).

In-Memory Accelerator Controller

☁️ Apache Ignite

TidalScale built software-defined server technology that aggregated the CPU, memory, and I/O of multiple commodity servers into a single large virtual machine (an inverse hypervisor / software-defined memory approach), enabling in-memory computing for very large workloads without specialized hardware.

MemristorLab — Interactive Memristive Neuromorphic Computing & In-Memory AI Laboratory. 6 modules: memristor physics, crossbar VMM, sneak path analysis, weight mapping, neuromorphic inference, energy arena. Zero dependencies.

Hardware-aware S4D state-space models on simulated analog memristor crossbars: a reproducible neuromorphic in-memory computing co-design study (PyTorch, CPU-only).

Device-to-algorithm neuromorphic: train spiking neural nets in snnTorch, then deploy on a simulated memristor crossbar with measured SnS2 device non-idealities.

in-depth analysis and comparison of eight leading in-memory subgraph matching algorithms, QuickSI, GraphQL, CFL, CECI, DP-iso, RI, and VF2++.

We present an analog in-memory computing (AIMC) evaluation framework, providing SW/HW performance for LLM inference.

GridGain is a unified real-time data platform that provides in-memory computing for transactions, analytics, and AI workloads. Built on top of Apache Ignite, it offers distributed database, caching, and computing capabilities for high-performance data-intensive applications.