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."
Apache Ignite Website
The DaiSy Library for Fast and Exact, Data Series and Vector Similarity Search
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
A bio-inspired analog compute substrate that learns on-chip — online, local, and forward-only. Weights live as analog charge on capacitors (compute-in-memory); an unsupervised forward-only front (SCFF) does ~80% of the work, a small gradient-descent namer the rest. A chip-design bet, not an ML model.
☁️ Apache Ignite
Software side of an LLM running inside a DRAM chip via charge-sharing PIM (BitNet b1.58 on DRAM-Bender silicon).
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.
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.
Device-to-algorithm neuromorphic: train spiking neural nets in snnTorch, then deploy on a simulated memristor crossbar with measured SnS2 device non-idealities.
Building analytical data models in SAP HANA. Developed database artifacts and visualizations from the Global Bike Inc. (GBI) enterprise data to translate complex sales data into clear, decision-ready business insights.
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.
Cache optimization and in-memory computing research focusing on performance, latency, and energy-efficient hybrid memory systems.
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