As a founding member of the 4-bits/cell NAND flash memory controller team at Samsung Electronics, I delved into the challenges of flash memory reliability—a pivotal research topic.

A Flash-SRAM-ADC-Fused Plastic Computing-in-Memory Macro for Learning in Neural Networks in a Standard 14nm FinFET Process

ANAFLASH is sponsoring the upcoming TinyML Summit. We welcome AI suppliers, end-users, developers, and business leaders interested in on-device AI.

We won a U.S. National Science Foundation (NSF) Small Business Innovation Research (SBIR) Phase II supplementary funding for $500,000 to conduct research and development (R&D) on energy efficient non-volatile neural network microprocessor for battery powered edge devices.

ANAFLASH received a funding from Plug and Play Ventures and joined its global startup acceleration ecosystem.

eF2lowSim: System-Level Simulator of eFlash-Based Compute-in-Memory Accelerators for Convolutional Neural Networks

A 333TOPS/W Logic-Compatible Multi-Level Embedded Flash Compute-In-Memory Macro with Dual-Slope Computation

Silicon Valley Start-Up AnaFlash Acquired Exclusive License for Single-poly Based Embedded Flash Memory Technology from University of Minnesota

Discover how we are revolutionizing the edge computing industry with our cutting-edge embedded Non-Volatile Memory technology, exclusively licensed from the University of Minnesota.

Embedded AI processor developer Anaflash Inc. (Sunnyvale, Calif.) is working with Samsung Foundry to develop embedded flash memory IP.

Silicon Valley Startup ANAFLASH Brings Cost-Effective Embedded Non-Volatile Memory Technology to Market

ANAFLASH has moved to Plug and Play Tech Center located at Sunnyvale, and shared the company's energy efficient edge computing vision during the Plug and Play Silicon Valley Summit.

ANAFLASH has prototyped a standard logic based non-volatile neural network accelerator with its commercialization partners and presented it at the Flash Memory Summit 2022.

Three patents about our standard logic-based neural network computing and logic-compatible non-volatile memory technology were granted by the USPTO.

We have been featured as one of the most innovative San Jose based energy companies and startups.

We have been with KIC Silicon Valley who helps engaging with global customers and investors. Through the support from KIC, we collaborate with promising AI technology startups in Korea.

An Embedded nand Flash-Based Compute-In-Memory Array Demonstrated in a Standard Logic Process

EE Times included us as a Silicon 100 Startup Worth Watching in 2021 and featured us as a "specialist processors and accelerators" company that develops an AI accelerator chip.

Cadence Design Systems Inc. featured us as a brain inspired processor developer for edge AI devices, supporting us with necessary EDA tools through its Incubator Program.

We investigated a radiation-tolerant neuromorphic processor featuring nonvolatile Compute-in-Memory (CiM) architecture under NASA's SBIR contract.

The researchers from MIT featured our chip as a hot AI chip to look along with ones from Qualcomm, Intel, and others at 2020 IEEE High Performance Extreme Computing Conference.

We received the SBIR Phase II grant from the National Science Foundation to conduct research and development on the logic compatible non-volatile neural network accelerator.

ANAFLASH has been listed along with many other great semiconductor startup companies in the Silicon 100 by the EE Times.

ANAFLASH is a Spring 2020 HotDesk (formerly Fall 2019 Cohort) company of the Berkeley SkyDeck in its CHIP TRACK program.

A 3D NAND Flash Ready 8-Bit Convolutional Neural Network Core Demonstrated in a Standard Logic Process

Demonstration of a Passive IC Tamper Sensor Based on an Exposed Floating Gate Device in a Standard Logic Process

A 68 Parallel Row Access Neuromorphic Core with 22K Multi-Level Synapses Based on Logic-Compatible Embedded Flash Memory Technology

Searching for the Perfect Artificial Synapse for AI Researchers tried out several new devices to get closer to the ideal needed for deep learning and neuromorphic computing