Apache Ignite
Apache Ignite 3
A secure, high-performance, multi-tenant platform for remote App ”Hosting“ 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
A computational foundation for AGI, based on hyperdimensional computing and framed in set theory.
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
☁️ Apache Ignite
A free, picture-first beginner guide to memristors and ReRAM, with interactive experiments and an LTspice model.
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++.
Hardware-aware S4D state-space models on simulated analog memristor crossbars: a reproducible neuromorphic in-memory computing co-design study (PyTorch, CPU-only).
Behavioral compute-SNR analysis and website for voltage-driven resistive crossbar IMCs
We present an analog in-memory computing (AIMC) evaluation framework, providing SW/HW performance for LLM inference.
TidalScale — independent third-party profile of a public API surface, by API Evangelist. 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 larg
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