Lauro Rizzatti Training and inferencing comprise two crucial aspects of AI processing in datacenters. Learn the differences between the two, and the cost-efficiency issues involved. The execution of artificial intelligence (AI) workloads in datacenters (Figure 1) involves two crucial processes: training and inference. At first glance, these processes appear similar—both involve reading data, processing it, and generating … Read more
Part 2 of 2 – Performance Validation Across Hardware Blocks and Firmware in SoC Designs Part 2 explores the performance validation process across hardware blocks and firmware in System-on-Chip (SoC) designs, emphasizing the critical role of Hardware-Assisted Verification (HAV) platforms. It outlines the validation workflow driven by real-world applications, and best practices for leveraging HAV … Read more
The rapid evolution of artificial intelligence (AI) has been marked by the rise of large language models (LLMs) with ever-growing numbers of parameters. From early iterations with millions of parameters to today’s tech giants boasting hundreds of billions or even trillions, the sheer scale of these models is staggering. Table 1 outlines the number of parameters … Read more
Part 1 of 2 – Essential Performance Metrics to Validate SoC Performance Analysis Part 1 provides an overview of the key performance metrics across three foundational blocks of System-on-Chip (SoC) designs that are vital for success in the rapidly evolving semiconductor industry and presents a holistic approach to optimize SoC performance, highlighting the need for … Read more
The Impact of AI on Software and Hardware Development Part 4 of this series analyzes how AI algorithmic processing is transforming software structures and significantly modifying processing hardware. It explores the marginalization of the traditional CPU architecture and demonstrates how software is increasingly dominating hardware. Additionally, it examines the impact of these changes on software … Read more
Designing a truly autonomous vehicle will require harnessing generative AI to enhance the capabilities of predictive AI. The journey toward achieving fully autonomous driving (AD) is progressing through five levels of increasing automation, codified by the Society of Automotive Engineers (SAE) in 2014 under Standard J3016. Beginning at Level 1 (L1), characterized by basic driver … Read more
Day in, day out, media outlets report an escalating electricity shortage across developed countries, and data centers often lie at the heart of these discussions. An often-repeated analogy compares the annual energy consumption of the largest data centers to that of major cities or even small countries. Compounding the issue are rising concerns that rural … Read more
Part 3 of this 4-part series analyzes methods and tools involved in debugging software at different layers of the software stack. Software debugging involves identifying and resolving issues ranging from functional misbehaviors to crashes. The essential requirement for validating software programs is the ability to monitor code execution on the underlying processor(s). Software debugging practices … Read more
Virtual Prototyping and Hybrid Emulation for Early Software Validation Debugging a software stack for complex System-on-Chip (SoC) designs is a highly iterative process, often requiring the execution of trillions of verification cycles even before end-user applications are considered. For instance, booting Android can take anywhere from 20 seconds to one minute at 1 GHz, depending … Read more
Despite seven decades of mostly unsuccessful investigation, AI has experienced significant growth over the last 10 years, expanding at an exponential rate. This escalating adoption has been propelled by a shift toward highly parallel computing architectures, a departure from conventional CPU-based systems. Traditional CPUs, with their sequential processing nature that handles one instruction at a … Read more