ML Opening New Doors For FPGAs

By Bryon Moyer - 24 Jun, 2020 - Comments: 0

FPGAs have long been used in the early stages of any new digital technology, given their utility for prototyping and rapid evolution. But with machine learning, FPGAs are showing benefits beyond those of more conventional solutions. This opens up a hot new market for FPGAs, which traditionally have been hard to sustain in high-volume production due to pricing, and hard to use for battery-dri... » read more

3 Challenges In Edge Designs

By Ed Sperling - 15 Jun, 2020 - Comments: 0

As companies begin exploring what will be necessary to win at the edge, they are coming up with some daunting challenges. Designing chips for the edge is far different than for the IoT/IIoT. The idea with the IoT was that simple sensors would relay data through a gateway to the cloud, where it would be processed and data could be sent back to the device as needed. That works if it's a small ... » read more

Week In Review: Auto, Security, Pervasive Computing

By Susan Rambo - 12 Jun, 2020 - Comments: 0

Edge, cloud, data center Programmable logic company Efinix used Cadence’s Digital Full Flow to finish Efinix’s Trion FPGA family for edge computing, AI/ML and vision processing applications, according to a press release. Last week Efinix also announced three software defined SoCs based on the RISC-V core. The SoCs are optimized to the Trion FPGAs. AI, machine learning Amazon will tempo... » read more

Hardware Security For AI Accelerators

By Rambus - 03 Jun, 2020 - Comments: 0

Dedicated accelerator hardware for artificial intelligence and machine learning (AI/ML) algorithms are increasingly prevalent in data centers and endpoint devices. These accelerators handle valuable data and models, and face a growing threat landscape putting AI/ML assets at risk. Using fundamental cryptographic security techniques performed by a hardware root of trust can safeguard these as... » read more

Powering The Edge

By Arm - 13 May, 2020 - Comments: 0

On-device machine learning (ML) is a phenomenon that has exploded in popularity. Smart devices that are able to make independent decisions, acting on locally generated data, are hailed as the future of compute for consumer devices: on-device processing slashes latency; increases reliability and safety; boosts privacy and security...all while saving on power and cost. Although ML in edge d... » read more

What Is DRAM’s Future?

By Bryon Moyer - 09 Apr, 2020 - Comments: 0

Memory — and DRAM in particular — has moved into the spotlight as it finds itself in the critical path to greater system performance. This isn't the first time DRAM has been the center of attention involving performance. The problem is that not everything progresses at the same rate, creating serial bottlenecks in everything from processor performance to transistor design, and even the t... » read more

AI Requires Tailored DRAM Solutions

By Rambus - 08 Apr, 2020 - Comments: 0

For over 30 years, DRAM has continuously adapted to the needs of each new wave of hardware spanning PCs, game consoles, mobile phones and cloud servers. Each generation of hardware required DRAM to hit new benchmarks in bandwidth, latency, power or capacity. Looking ahead, the 2020s will be the decade of artificial intelligence/machine learning (AI/ML) touching every industry and applicatio... » read more

More Multiply-Accumulate Operations Everywhere

By Ed Sperling - 02 Apr, 2020 - Comments: 1

Geoff Tate, CEO of Flex Logix, sat down with Semiconductor Engineering to talk about how to build programmable edge inferencing chips, embedded FPGAs, where the markets are developing for both, and how the picture will change over the next few years. SE: What do you have to think about when you're designing a programmable inferencing chip? Tate: With a traditional FPGA architecture you ha... » read more

HBM Issues In AI Systems

By Brian Bailey - 16 Mar, 2020 - Comments: 0

All systems face limitations, and as one limitation is removed, another is revealed that had remained hidden. It is highly likely that this game of Whac-A-Mole will play out in AI systems that employ high-bandwidth memory (HBM). Most systems are limited by memory bandwidth. Compute systems in general have maintained an increase in memory interface performance that barely matches the gains in... » read more

How Much Power Will AI Chips Use?

By Ed Sperling - 12 Mar, 2020 - Comments: 1

AI and machine learning have voracious appetites when it comes to power. On the training side, they will fully utilize every available processing element in a highly parallelized array of processors and accelerators. And on the inferencing side they, will continue to optimize algorithms to maximize performance for whatever task a system is designed to do. But as with cars, mileage varies gre... » read more

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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

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

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.