tag: data movement

Workload-Specific Data Movements Across AI Workloads in Multi-Chiplet AI Accelerators
By Technical Paper Link - 04 Nov, 2024 - Comments: 0

A new technical paper titled "Communication Characterization of AI Workloads for Large-scale Multi-chiplet Accelerators" was published by researchers at Universitat Politecnica de Catalunya. Abstract "Next-generation artificial intelligence (AI) workloads are posing challenges of scalability and robustness in terms of execution time due to their intrinsic evolving data-intensive characteris... » read more

Higher Density, More Data Create New Bottlenecks In AI Chips
By Adam Kovac - 12 Sep, 2024 - Comments: 0

Data movement is becoming a bigger problem at advanced nodes and in advanced packaging due to denser circuitry, more physical effects that can affect the integrity of signals or the devices themselves, and a significant increase in data from AI and machine learning. Just shrinking features in a design is no longer sufficient, given the scaling mismatch between SRAM-based L1 cache and digital... » read more

The Journey To Exascale Computing And Beyond
By Steven Woo - 14 Dec, 2023 - Comments: 0

High performance computing witnessed one of its most ambitious leaps forward with the development of the US supercomputer “Frontier.” As Scott Atchley from Oak Ridge National Laboratory discussed at Supercomputing 23 (SC23) in Denver last month, the Frontier had the ambitious goal of achieving performance levels 1000 times higher than the petascale systems that preceded it, while also stayi... » read more

Designing for Data Flow
By Marie C. Baca - 02 Mar, 2023 - Comments: 0

Movement and management of data inside and outside of chips is becoming a central theme for a growing number of electronic systems, and a huge challenge for all of them. Entirely new architectures and techniques are being developed to reduce the movement of data and to accomplish more per compute cycle, and to speed the transfer of data between various components on a chip and between chips ... » read more

Dealing With Heat In Near-Memory Compute Architectures
By Karen Heyman - 13 Oct, 2022 - Comments: 0

The explosion in data forcing chipmakers to get much more granular about where logic and memory are placed on a die, how data is partitioned and prioritized to utilize those resources, and what the thermal impact will be if they are moved closer together on a die or in a package. For more than a decade, the industry has faced a basic problem — moving data can be more resource-intensive tha... » read more

Algorithm HW Framework That Minimizes Accuracy Degradation, Data Movement, And Energy Consumption Of DNN Accelerators (Georgia Tech)
By Technical Paper Link - 31 Aug, 2022 - Comments: 0

This new research paper titled "An Algorithm-Hardware Co-design Framework to Overcome Imperfections of Mixed-signal DNN Accelerators" was published by researchers at Georgia Tech. According to the paper's abstract, "In recent years, processing in memory (PIM) based mixed-signal designs have been proposed as energy- and area-efficient solutions with ultra high throughput to accelerate DNN com... » read more

Moving Intelligence To The Edge
By Ed Sperling - 05 Apr, 2022 - Comments: 0

The buildout of the edge is driving a slew of new challenges and opportunities across the chip industry. Sailesh Chittipeddi, executive vice president at Renesas Electronics America, talks about the shift toward more AI-centric workloads rather than CPU-centric, why embedded computing is becoming the foundation of all intelligences, and the importance of software, security, and user experience ... » read more

Seven Hardware Advances We Need to Enable The AI Revolution
By Remy Pottier - 10 Mar, 2022 - Comments: 1

The potential, positive impact AI will have on society at large is impossible to overestimate. Pervasive AI, however, remains a challenge. Training algorithms can take inordinate amounts of power, time, and computing capacity. Inference will also become more taxing with applications such as medical imaging and robotics. Applied Materials estimates that AI could consume up to 25% of global elect... » read more

1.6 Tb/s Ethernet Challenges
By Ed Sperling - 16 Nov, 2021 - Comments: 0

Moving data at blazing fast speeds sounds good in theory, but it raises a number of design challenges. John Swanson, senior product marketing manager for high-performance computing digital IP at Synopsys, talks about the impact of next-generation Ethernet on switches, the types of data that need to be considered, the causes of data growth, and the size and structure of data centers, both in the... » read more

Optimization Driving Changes In Microarchitectures
By Ann Mutschler - 23 Sep, 2021 - Comments: 0

The semiconductor ecosystem is at a turning point for how to best architect the CPU based on the explosion of data, the increased usage of AI, and the need for differentiation and customization in leading-edge applications. In the past, much of this would have been accomplished by moving to the next process node. But with the benefits from scaling diminishing at each new node, the focus is s... » read more

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