3nm: Blurring Lines Between SoCs, PCBs And Packages

By Ann Mutschler - 02 Mar, 2020 - Comments: 5

Leading-edge chipmakers, foundries and EDA companies are pushing into 3nm and beyond, and they are encountering a long list of challenges that raise questions about whether the entire system needs to be shrunk onto a chip or into a package. For 7nm and 5nm, the problems are well understood. In fact, 5nm appears to be more of an evolution from 7nm than a major shift in direction. But at 3nm, ... » read more

The Challenges Of Building Inferencing Chips

By Ann Mutschler - 27 Feb, 2020 - Comments: 0

Putting a trained algorithm to work in the field is creating a frenzy of activity across the chip world, spurring designs that range from purpose-built specialty processors and accelerators to more generalized extensions of existing and silicon-proven technologies. What's clear so far is that no single chip architecture has been deemed the go-to solution for inferencing. Machine learning is ... » read more

Machine Learning Inferencing Moves To Mobile Devices

By Kevin Fogarty - 02 Jul, 2019 - Comments: 0

It may sound retro for a developer with access to hyperscale data centers to discuss apps that can be measured in kilobytes, but the emphasis increasingly is on small, highly capable devices. In fact, Google staff research engineer Pete Warden points to a new app that uses less than 100 kilobytes for RAM and storage, creates an inference model smaller than 20KB, and which is capable of proce... » read more

Processing In Memory

By Ed Sperling - 05 Sep, 2018 - Comments: 1

Adding processing directly into memory is getting a serious look, particularly for applications where the volume of data is so large that moving it back and forth between various memories and processors requires too much energy and time. The idea of inserting processors into memory has cropped up intermittently over the past decade as a possible future direction, but it was dismissed as an e... » read more

Pros, Cons Of ML-Specific Chips

By Ed Sperling - 27 Jul, 2018 - Comments: 1

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. To view part one, click here. Part two is here. SE: Is the industry's knowledge of machine learning keeping up with th... » read more

Architecting For AI

By Ann Mutschler - 17 Jul, 2018 - Comments: 0

Semiconductor Engineering sat down to talk about what is needed today to enable artificial intelligence training and inferencing with Manoj Roge, vice president, strategic planning at Achronix; Ty Garibay, CTO at Arteris IP; Chris Rowen, CEO of Babblelabs; David White, distinguished engineer at Cadence; Cheng Wang, senior VP engineering at Flex Logix; and Raik Brinkmann, president and CEO of O... » read more

Security Holes In Machine Learning And AI

By Ed Sperling - 09 Jul, 2018 - Comments: 0

Machine learning and AI developers are starting to examine the integrity of training data, which in some cases will be used to train millions or even billions of devices. But this is the beginning of what will become a mammoth effort, because today no one is quite sure how that training data can be corrupted, or what to do about it if it is corrupted. Machine learning, deep learning and arti... » read more

Machine Learning’s Limits

By Ed Sperling - 03 Jul, 2018 - Comments: 0

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. To view part one, click here. SE: How much of what goes wrong in machine learning depends on the algorithm being wrong... » read more

Machine Learning’s Limits (Part 1)

By Ed Sperling - 07 Jun, 2018 - Comments: 0

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. SE: Where are we with machine learning? What problems still have to be resolved? Aitken: We're in a state where thi... » read more

Babblelabs: Deep Learning Speech Processing

By Ann Mutschler - 26 Jan, 2018 - Comments: 0

Pronounced “babble labs,” a startup that is the brainchild of serial entrepreneur [getperson id="11244" comment="Chris Rowen"] is setting out to transform speech processing and will leverage deep learning to do so. Rowen, CEO of Babblelabs, has spoken for some time about move of processing to more general purpose hardware, with applications layered on top, so it’s not so surprising his... » read more

Knowledge Centers
Entities, people and technologies explored

Shift Left Is The Tip Of The Iceberg

A transformative change is underway for semiconductor design and EDA. New languages, models, and abstractions will need to be created.

by Brian Bailey

Partitioning In The Chiplet Era

Understanding how chiplets interact under different workloads is critical to ensuring signal integrity and optimal performance in heterogeneous designs.

by Ann Mutschler

NAND Flash Targets 1,000 Layers

New techniques go beyond improved deposition and etching, but challenges stack up, too.

by Bryon Moyer

3.5D: The Great Compromise

Pros and cons of a middle-ground chiplet assembly that combines 2.5D and 3D-IC.

by Ed Sperling

AI’s Role In Chip Design Widens, Drawing In New Startups

Focus is on letting engineers do much more with the same or fewer resources — and less drudgery.

by Karen Heyman

What Comes After HBM For Chiplets

The standard for high-bandwidth memory limits design freedom at many levels, but that is required for interoperability. What freedoms can be taken from other functions to make chiplets possible?

by Brian Bailey

Memory Fundamentals For Engineers

eBook: Nearly everything you need to know about memory, including detailed explanations of the different types of memory; how and where these are used today; what's changing, which memories are successful and which ones might be in the future; and the limitations of each memory type.

by The SE Staff

New AI Processors Architectures Balance Speed With Efficiency

Hot Chips 24: Large language models ratchet up pressure for sustainable computing and heterogeneous integration; data management becomes key differentiator.