Are We Too Hard On Artificial Intelligence For Autonomous Driving?


I recently attended and presented at Detroit's "Implementation of ISO 26262 & SOTIF" conference. Its subtitle was "Taking an Integrated Approach to Automotive Safety." After three days, my head was spinning with numbers of ISO/SAE and other standards. And at the end of day two, after yet another example that tricked autonomous driving prototypes into behaving wrongly, I sighed and asked whe... » read more

Chip Design Shifts As Fundamental Laws Run Out Of Steam


Dennard scaling is gone, Amdahl's Law is reaching its limit, and Moore's Law is becoming difficult and expensive to follow, particularly as power and performance benefits diminish. And while none of that has reduced opportunities for much faster, lower-power chips, it has significantly shifted the dynamics for their design and manufacturing. Rather than just different process nodes and half ... » read more

Securing Accelerator Blades For Datacenter AI/ML Workloads


Data centers handle huge amounts of AI/ML training and inference workloads for their individual customers. Such a vast number of workloads calls for efficient processing, and to handle these workloads we have seen many new solutions emerge in the market. One of these solutions is pluggable accelerator blades, often deployed in massively parallel arrays, that implement the latest state-of-the-ar... » read more

Week In Review: Design, Low Power


Tools and IP Electronic system design revenue hit a record $3.75 billion in the second quarter, according to a report from ESD Alliance, a SEMI Technology Community. That number represents a 17.5% year-over-year increase. Walden C. Rhines, the report’s executive sponsor, said it was the largest such jump in over a decade and that all product categories and geographic regions recorded second ... » read more

Research Bits: Oct. 18


Modular AI chip Engineers at the Massachusetts Institute of Technology (MIT), Harvard University, Stanford University, Lawrence Berkeley National Laboratory, Korea Institute of Science and Technology, and Tsinghua University created a modular approach to building stackable, reconfigurable AI chips. The design comprises alternating layers of sensing and processing elements, along with LEDs t... » read more

HBM3 In The Data Center


Frank Ferro, senior director of product management at Rambus, talks about the forthcoming HBM3 standard, why this is so essential for AI chips and where the bottlenecks are today, what kinds of challenges are involved in working with this memory, and what impact chiplets and near-memory compute will have on HBM and bandwidth.     » read more

Complex Tradeoffs In Inferencing Chips


Designing AI/ML inferencing chips is emerging as a huge challenge due to the variety of applications and the highly specific power and performance needs for each of them. Put simply, one size does not fit all, and not all applications can afford a custom design. For example, in retail store tracking, it's acceptable to have a 5% or 10% margin of error for customers passing by a certain aisle... » read more

Designing A Better Clock Network


Laying the proper clock network architecture foundation makes all the difference for the best performance, power, and timing of a chip, particularly in advanced node SoCs packed with billions of transistors. Each transistor, which acts like a standard cell, needs a clock. An efficient clock network should ensure the switching transistors save power. In today’s advanced nodes, when a design... » read more

Beyond Autonomous Cars


As the automotive industry takes a more measured approach to self-driving cars and long-haul trucks for safety and security reasons, there is a renewed focus on other types of vehicles utilizing autonomous technology. The list is long and growing. It now includes autonomous trains, helicopters, tractors, ships, submarines, drones, delivery robots, motorcycles, scooters, and bikes, all of whi... » read more

Training a ML model On An Intelligent Edge Device Using Less Than 256KB Memory


A new technical paper titled "On-Device Training Under 256KB Memory" was published by researchers at MIT and MIT-IBM Watson AI Lab. “Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a bro... » read more

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