Senior Embedded Software Engineer

Mythic selects memBrain technology from Silicon Storage Technology for its next generation of ultra-low-power Analog Processing Units. Mythic has chosen memBrain neuromorphic hardware intellectual property (IP) from Microchip Technology's Silicon Storage Technology (SST) subsidiary for its next-generation edge to enterprise Analog Processing Units (APUs). Mythic will utilize SST's SuperFlash embedded non-volatile memory (eNVM) bitcells to deliver high levels of analog compute-in-memory (aCIM) performance per watt. The partnership enables Mythic to achieve 120 TOPS/watt inference processing for power-efficient AI acceleration at the edge and in the data center: Mythic's APUs are targeted to be up to 100 times more energy-efficient than conventional digital Graphics Processing Units (GPUs). One hundred fifty billion units of SST SuperFlash technology that Mythic is licensing have been shipped to date. SuperFlash technology is the de facto eNVM solution for a broad spectrum of industries including industrial, automotive, consumer and computing for critical data and code storage, and is licensed by all of the top ten semiconductor foundries worldwide. "Mythic is pioneering innovative solutions in AI inference processing and AI sensor fusion for industrial, automotive and data center applications, effectively overcoming current AI power limitations," said Mark Reiten, vice president of Microchip's Edge AI business unit. "As the core memory technology for Mythic's next-generation products, memBrain delivers significant power efficiency and high performance for both edge and data center applications." The memBrain cell features: * Up to 8 data bits per bitcell (8 bpc) storage * Single digit nanoamp (nA) bitcell read current * 10-year data retention at operating temperature * 100,000 endurance cycles * Full state machine control of the 8 bpc multi-state write operation * Single cycle multiply-and-accumulate operations for aCIM "Mythic selected SST after an industry-wide search of eNVM technologies and determined the memBrain cell technology best enabled us to achieve the ultra-low-power and high performance required by our customers," said Dr. Taner Ozcelik, Mythic's chief executive officer. "Additionally, the wide foundry availability of its industry-proven SuperFlash technology, coupled with the outstanding support of the SST engineering team has been invaluable during our product development cycle." SST's memBrain technology has been developed and deployed in 40 nm and 28 nm foundry processes using production-ready SuperFlash memory. 22 nm memBrain development is planned to extend the technology roadmap. Designed to provide reliable, high-performance and low-power non-volatile storage directly on the chip, SuperFlash memory is widely used in applications that require fast access times, high endurance and data retention without the need for external memory components. Pricing and Availability Customers interested in SST's memBrain solutions and SuperFlash technology should access the SST website or contact a regional SST sales executive for details. Those interested in Mythic's products should visit the Mythic website or contact Taner Ozcelik at [email protected].

SoftBank-backed AI chip firm Mythic raises $125m to rival Nvidia. Mythic has raised US$125 million in a funding round led by DCVC to advance its AI chip technology and compete with Nvidia. Investors in the round include New Enterprise Associates, Atreides Management, SoftBank Group, Honda Motor, and Lockheed Martin. Mythic develops analog computers designed to process large AI data sets with lower power usage than traditional digital CPUs. The company appointed Taner Ozcelik, a former Nvidia executive, as CEO in 2024. Mythic plans to use the new capital to commercialize its products and expand its customer base. Food for thought. * The company says its analog processing units (APUs) reach 120 trillion operations per second per watt, about 100x the energy efficiency of top GPUs 1, and can process up to 750x more tokens (the chunks of text these models read and generate) per second per watt than Nvidia's highest-end GPUs on large language models 2. The 750x figure comes from internal tests on production silicon validated by customers, with no public MLPerf or other third-party results for independent checks against Nvidia 2. * Software supports ONNX (Open Neural Network Exchange, an open model format) and NVIDIA TensorRT (an inference optimizer for Nvidia GPUs), plus mainstream frameworks 2. * The company cites validation by the U.S. Department of Defense, auto original equipment manufacturers (OEMs), and defense partners 2. Honda Motor and Lockheed Martin are strategic investors 2. The silicon is in production, and the company raised $125 million, yet it has not named paying customers or design wins 2. * Data centers face rising power limits as AI could use 10% of U.S. electricity by decade's end 1. That pressure prompts teams to look past GPU-heavy inference infrastructure (running trained models to generate outputs). * System integrators (IT services firms that design and deploy hardware/software stacks) with MLOps teams can offer model porting, optimization, plus benchmarking on non-Nvidia accelerators such as Mythic's APUs 2. * Service teams can test whether analog compute or other inference chips cut operating costs plus power use for select edge workloads (on-device or near where data is generated) 2. Early skill in quantization (reducing numerical precision to improve efficiency) and retraining workflows (fine-tuning models after conversion) can set these firms apart 3. How would you feel if you could no longer use Tech in Asia?