Expedera Inc.

Whether deployed in-cabin for driver distraction or in the ADAS stack for object recognition and point cloud processing, AI forms the backbone of the future of safer, smarter cars. Read more about how Expedera's Origin Evolution for Automotive is the ideal AI processing solution for today and tomorrow's vehicles. https://lnkd.in/gsxCF4bx

ICYMI: WHITE PAPER ALERT: Since the groundbreaking 2017 publication of “Attention Is All You Need,” the transformer architecture has fundamentally reshaped artificial intelligence research and development. This innovation laid the foundation for Large Language Models (LLMs) and Video Language Models (VLMS), fueling a wave of productization across the industry. A defining milestone was the public launch of ChatGPT in November 2022, which brought transformer-powered AI into mainstream use. Since then, LLMs have enabled a broad spectrum of applications, from conversational agents to advancements in medical research. However, running these LLMs efficiently presents substantial challenges, particularly on edge computing devices and legacy hardware architectures that were designed before the widespread adoption of large language models. This white paper will explore these issues and how Expedera addresses them with its Origin Evolution™ architecture. https://lnkd.in/gcrvnye5

Complex model architectures, demanding runtime computations, and transformer-specific operations introduce unique challenges to running LLM's at the edge. In a Semiconductor Engineering blog, Expedera Inc. explores how LLMs challenge edge processing and solutions to the issues created. https://lnkd.in/grkuPQW8 #LLM #NPU #edgeAI

For years, processors focused on performance, and that performance had little accountability to anything else. Performance still matters, but now it must be accountable to power. In a recent feature, Semiconductor Engineering's explores "Can Today’s Processor Architectures Be More Efficient?" with comments from Expedera Inc.'s VP of Marketing Paul Karazuba https://lnkd.in/d58-evkd

Expedera is proud to be featured again in EE Times' annual "Silicon 100: Startups to Watch" feature. Download the report from our friends at EE Times at: https://lnkd.in/e9c4NR3u

ICYMI: Expedera introduces our newest architecture leap, the Origin Evolution. LLM-focused, but with full support for traditional CNN and RNNs, Origin Evolution addresses the needs of both edge device and data center hardware. Learn a whole lot more about Evolution in our latest white paper: https://lnkd.in/gcrvnye5

WHITE PAPER ALERT: Since the groundbreaking 2017 publication of “Attention Is All You Need,” the transformer architecture has fundamentally reshaped artificial intelligence research and development. This innovation laid the foundation for Large Language Models (LLMs) and Video Language Models (VLMS), fueling a wave of productization across the industry. A defining milestone was the public launch of ChatGPT in November 2022, which brought transformer-powered AI into mainstream use. Since then, LLMs have enabled a broad spectrum of applications, from conversational agents to advancements in medical research. However, running these LLMs efficiently presents substantial challenges, particularly on edge computing devices and legacy hardware architectures that were designed before the widespread adoption of large language models. This white paper will explore these issues and how Expedera addresses them with its Origin Evolution™ architecture. https://lnkd.in/gcrvnye5

New Embedded Vision Summit Presentation from Expedera Inc.: "Evolving Inference Processor Software Stacks to Support LLMs" https://lnkd.in/g9kQuJvT

Nearly all the data input for AI so far has been text, but that's about to change. In the future, that input likely will include video, voice, as well as other types of data, causing a massive increase in the amount of data that needs to be modeled and the compute resources necessary to make it all work. This is hard enough in hyperscale data centers, which are sprouting up everywhere to handle the training and some inferencing, but it's even more of a challenge in bandwidth- and power-limited edge devices. Sharad Chole, chief scientist and co-founder of Expedera Inc., talks with Semiconductor Engineering about the tradeoffs involved in making this work, how to reduce the size of LLMs, and what impact this will have on engineers working in this space. https://lnkd.in/gVNzBFMw

Artificial intelligence (AI) involves intense computing and tons of data. The computing may be performed by CPUs, GPUs, or dedicated accelerators, and while the data travels through DRAM on its way to the processor, the best DRAM type for this purpose depends on the type of system that is performing the training or inference. In a special report, Semiconductor Engineering explores "The Best DRAMs For AI", with comments from Expedera Inc.'s Ramteja Tadishetti https://lnkd.in/gu8BDtrt