Neural Networks Without Matrix Math

By Katherine Derbyshire - 17 Sep, 2020 - Comments: 0

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. The network first analyzes a training example, typically assign... » read more

IBM Takes AI In Different Directions

By Ed Sperling - 06 Jun, 2018 - Comments: 3

Jeff Welser, vice president and lab director at IBM Research Almaden, sat down with Semiconductor Engineering to discuss what's changing in artificial intelligence and what challenges still remain. What follows are excerpts of that conversation. SE: What's changing in AI and why? Welser: The most interesting thing in AI right now is that we've moved from narrow AI, where we've proven you... » read more

3D Neuromorphic Architectures

By Katherine Derbyshire - 11 Dec, 2017 - Comments: 1

Matrix multiplication is a critical operation in conventional neural networks. Each node of the network receives an input signal, multiplies it by some predetermined weight, and passes the result to the next layer of nodes. While the nature of the signal, the method used to determine the weights, and the desired result will all depend on the specific application, the computational task is simpl... » 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

Why Small Fab And Assembly Houses Are Thriving

Megafabs churning out the most advanced processors are not the only game in town.

by Bryon Moyer

tag: Kirchoff’s Law

Neural Networks Without Matrix Math

IBM Takes AI In Different Directions

3D Neuromorphic Architectures