Power Becomes Bigger Concern For Embedded Processors


Power is emerging as the dominant concern for embedded processors even in applications where performance is billed as the top design criteria. This is happening regardless of the end application or the process node. In some high-performance applications, power density and thermal dissipation can limit how fast a processor can run. This is compounded by concerns about cyber and physical secur... » read more

Power-Hungry Safety And Security


There is a price to pay for everything. When it comes to adding safety and security into a device, the costs in terms of power and area can be significant, but if the task is taken seriously, those costs can be managed and minimized. New analysis and implementation tools are coming to market that can also help to keep the costs contained. But it also requires the right mindset. As more indus... » read more

Last-Level Cache


Kurt Shuler, vice president of marketing at Arteris IP, explains how to reduce latency and improve performance with last-level cache in order to avoid sending large amounts of data to external memory, and how to ensure quality of service on a chip by taking into account contention for resources. » read more

Timing Closure At 7/5nm


Mansour Amirfathi, director of application engineering at Synopsys, examines how to determine if assumptions about design are correct, how many cycles are needed for a particular operation and why this is so complicated, and what happens if signals get out of phase. » read more

HBM Issues In AI Systems


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?


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

Power Management Becomes Top Issue Everywhere


Power management is becoming a bigger challenge across a wide variety of applications, from consumer products such as televisions and set-top-boxes to large data centers, where the cost of cooling server racks to offset the impact of thermal dissipation can be enormous. Several years ago, low-power design was largely relegated to mobile devices that were dependent on a battery. Since then, i... » read more

Power Challenges In ML Processors


The design of artificial intelligence (AI) chips or machine learning (ML) systems requires that designers and architects use every trick in the book and then learn some new ones if they are to be successful. Call it style, call it architecture, there are some designs that are just better than others. When it comes to power, there are plenty of ways that small changes can make large differences.... » read more

High-Performance Memory For AI And HPC


Frank Ferro, senior director of product management at Rambus, examines the current performance bottlenecks in high-performance computing, drilling down into power and performance for different memory options, and explains what are the best solutions for different applications and why. » read more

Enterprise-Class DRAM Reliability


Brett Murdock, product manager for memory interfaces at Synopsys, examines demand for DDR5 and DDR4 in both on-premise and cloud implementations, what features are available for which versions, how they affect performance and power, how ECC is implemented, and how the data moves throughout these systems. » read more

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