tag: machine learning

Debug: The Schedule Killer
By Brian Bailey - 30 Jun, 2021 - Comments: 0

Debug often has been labeled the curse of management and schedules. It is considered unpredictable and often can happen close to the end of the development cycle, or even after – leading to frantic attempts at work-arounds. And the problem is growing. "Historically, about 40% of time is spent in debug, and that aspect is becoming more complex," says Vijay Chobisa, director of product manag... » read more

Manufacturing Bits: June 29
By Mark LaPedus - 29 Jun, 2021 - Comments: 0

Speeding up ALD with AI The U.S. Department of Energy’s (DOE) Argonne National Laboratory has developed various ways to make atomic layer deposition (ALD) more efficient by using artificial intelligence (AI). ALD is a deposition technique that deposits materials one layer at a time on chips. For years, ALD has been used for the production of DRAMs, logic devices and other products. In ... » read more

Architectural Considerations For AI
By Brian Bailey - 24 Jun, 2021 - Comments: 2

Custom chips, labeled as artificial intelligence (AI) or machine learning (ML), are appearing on a weekly basis, each claiming to be 10X faster than existing devices or consume 1/10 the power. Whether that is enough to dethrone existing architectures, such as GPUs and FPGAs, or whether they will survive alongside those architectures isn't clear yet. The problem, or the opportunity, is that t... » read more

CEO Outlook: More Data, More Integration, Same Deadlines
By Ed Sperling - 24 Jun, 2021 - Comments: 1

Experts at the Table: Semiconductor Engineering sat down to discuss the future of chip design and EDA tools with Lip-Bu Tan, CEO of Cadence; Simon Segars, CEO of Arm; Joseph Sawicki, executive vice president of Siemens IC EDA; John Kibarian, CEO of PDF Solutions; Prakash Narain, president and CEO of Real Intent; Dean Drako, president and CEO of IC Manage; and Babak Taheri, CEO of Silvaco. What ... » read more

Shifting Toward Data-Driven Chip Architectures
By Ed Sperling and Ann Mutschler - 16 Jun, 2021 - Comments: 0

An explosion in data is forcing chipmakers to rethink where to process data, which are the best types of processors and memories for different types of data, and how to structure, partition and prioritize the movement of raw and processed data. New chips from systems companies such as Google, Facebook, Alibaba, and IBM all incorporate this approach. So do those developed by vendors like Appl... » read more

NN-Baton: DNN Workload Orchestration & Chiplet Granularity Exploration for Multichip Accelerators
By Technical Paper Link - 14 Jun, 2021 - Comments: 0

"Abstract—The revolution of machine learning poses an unprecedented demand for computation resources, urging more transistors on a single monolithic chip, which is not sustainable in the Post-Moore era. The multichip integration with small functional dies, called chiplets, can reduce the manufacturing cost, improve the fabrication yield, and achieve die-level reuse for different system scales... » read more

Thermal Floorplanning For Chips
By Brian Bailey - 10 Jun, 2021 - Comments: 0

Heat management is becoming crucial to an increasing number of chips, and it's one of a growing number of interconnected factors that must be considered throughout the entire development flow. At the same time, design requirements are exacerbating thermal problems. Those designs either have to increase margins or become more intelligent about the way heat is generated, distributed, and dissi... » read more

There’s More To Machine Learning Than CNNs
By Bryon Moyer - 09 Jun, 2021 - Comments: 1

Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

Security Solutions for AI/ML
By Rambus - 02 Jun, 2021 - Comments: 0

AI/ML is increasingly pervasive across all industries driven by a massive wave of digitization. Data, the raw material of AI/ML and Deep Learning algorithms, is available in enormous quantities from all aspects of business operations. AI/ML promises great gains in responsiveness and adaptability in an ever-changing technology landscape, and industries are enthusiastically responding to that app... » read more

Monitoring Performance From Inside A Chip
By Ed Sperling - 01 Jun, 2021 - Comments: 0

Deep data, which is generated inside the chip rather than externally, is becoming more critical at each new process node and in advanced packages. Uzi Baruch, chief strategy officer at proteanTecs, talks with Semiconductor Engineering about using that data to identify potential problems before they result in failures in the field, and why it's essential to monitor these devices throughout their... » read more

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