Processor Tradeoffs For AI Workloads

AI is forcing fundamental shifts in chips used in data centers and in the tools used to design them, but it also is creating gaps between the speed at which that technology advances and the demands from customers. These shifts started gradually, but they have accelerated and multiplied over the past year with the rollout of ChatGPT and other large language models. There is suddenly much more... » read more

Co-Design View of Cross-Bar Based Compute-In-Memory

A new review paper titled "Compute in-Memory with Non-Volatile Elements for Neural Networks: A Review from a Co-Design Perspective" was published by researchers at Argonne National Lab, Purdue University, and Indian Institute of Technology Madras. "With an over-arching co-design viewpoint, this review assesses the use of cross-bar based CIM for neural networks, connecting the material proper... » read more

FP8: Cross-Industry Hardware Specification For AI Training And Inference (Arm, Intel, Nvidia)

Arm, Intel, and Nvidia proposed a specification for an 8-bit floating point (FP8) format that could provide a common interchangeable format that works for both AI training and inference and allow AI models to operate and perform consistently across hardware platforms. Find the technical paper titled " FP8 Formats For Deep Learning" here. Published Sept 2022. Abstract: "FP8 is a natural p... » read more

Improving Yield With Machine Learning

Machine learning is becoming increasingly valuable in semiconductor manufacturing, where it is being used to improve yield and throughput. This is especially important in process control, where data sets are noisy. Neural networks can identify patterns that exceed human capability, or perform classification faster. Consequently, they are being deployed across a variety of manufacturing proce... » read more

Deep Learning Applications For Material Sciences: Methods, Recent Developments

New technical paper titled "Recent advances and applications of deep learning methods in materials science" from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory, Carnegie Mellon University, Northwestern University, and Columbia University. Abstract "Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning... » read more

DRAM Thermal Issues Reach Crisis Point

Within the DRAM world, thermal issues are at a crisis point. At 14nm and below, and in the most advanced packaging schemes, an entirely new metric may be needed to address the multiplier effect of how thermal density increasingly turns minor issues into major problems. A few overheated transistors may not greatly affect reliability, but the heat generated from a few billion transistors does.... » read more

Why AI Systems Are So Hard To Predict

AI can do many things, but how to ensure that it does the right things is anything but clear. Much of this stems from the fact that AI/ML/DL systems are built to adapt and self-optimize. With properly adjusted weights, training algorithms can be used to make sure these systems don't stray too far from the starting point. But how to test for that, in the lab, the fab and in the field is far f... » read more

Using AI And Bugs To Find Other Bugs

Debug is starting to be rethought and retooled as chips become more complex and more tightly integrated into packages or other systems, particularly in safety- and mission-critical applications where life expectancy is significantly longer. Today, the predominant bug-finding approaches use the ubiquitous constrained random/coverage driven verification technology, or formal verification techn... » read more

Nvidia To Buy Arm For $40B

Nvidia inked a deal with Softbank to buy Arm for $40 billion, combining the No. 1 AI/ML GPU maker with the No. 1 processor IP company. Assuming the deal wins regulatory approval, the combination of these two companies will create a powerhouse in the AI/ML world. Nvidia's GPUs are the go-to platform for training algorithms, while Arm has a broad portfolio of AI/ML processor cores. Arm also ha... » read more

3 Challenges In Edge Designs

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

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