System Bits: May 14


Faster U.S. supercomputers on the way The U.S. Department of Energy awarded a contract for more than $600 million to Cray for an exascale supercomputer to be installed at the Oak Ridge National Laboratory during 2021. Cray will provide its Shasta architecture and Slingshot interconnect for what is dubbed the Frontier supercomputer. Advanced Micro Devices will have a key role in building the... » read more

Unsticking Moore’s Law


Sanjay Natarajan, corporate vice president at Applied Materials with responsibility for transistor, interconnect and memory solutions, sat down with Semiconductor Engineering to talk about variation, Moore's Law, the impact of new materials such as cobalt, and different memory architectures and approaches. What follows are excerpts of that conversation. SE: Reliability is becoming more of an... » read more

Making IP Friendlier


Semiconductor Engineering sat down to discuss IP tracking and management with Ranjit Adhikary, vice president of marketing for ClioSoft; Jim Bruister, director digital systems (since retired) at Silvaco; Marc Greenberg, product marketing group director at Cadence; and Kelvin Low, vice president of marketing at Arm. What follows are excerpts from that conversation. Part one can be found here. ... » read more

Will AI Drive Scaling Forward?


The almost ubiquitous rollout of AI and its offshoots—machine learning, deep learning, neural nets of all types—will require significantly more processing power as the amount of data that needs to be processed continues to grow by orders of magnitude. What isn't clear yet is how that will affect semiconductor manufacturing or how quickly that might happen. AI is more than the latest buz... » read more

AI Begins To Reshape Chip Design


Artificial intelligence is beginning to impact semiconductor design as architects begin leveraging its capabilities to improve performance and reduce power, setting the stage for a number of foundational shifts in how chips are developed, manufactured and updated in the future. AI—and machine learning and deep learning subsets—can be used to greatly improve the functional control and pow... » read more

Using ASICs For AI Inferencing


Flex Logix’s Cheng Wang looks at why ASICs are the best way to improve performance and optimize power and area for inferencing, and how to add flexibility into those designs to deal with constantly changing algorithms and data sets. https://youtu.be/XMHr7sz9JWQ » read more

Intel’s Next Move


Gadi Singer, vice president and general manager of Intel's Artificial Intelligence Products Group, sat down with Semiconductor Engineering to talk about Intel's vision for deep learning and why the company is looking well beyond the x86 architecture and one-chip solutions. SE: What's changing on the processor side? Singer: The biggest change is the addition of deep learning and neural ne... » read more

AI Architectures Must Change


Using existing architectures for solving machine learning and artificial intelligence problems is becoming impractical. The total energy consumed by AI is rising significantly, and CPUs and GPUs increasingly are looking like the wrong tools for the job. Several roundtables have concluded the best opportunity for significant change happens when there is no legacy IP. Most designs have evolved... » read more

Pros, Cons Of ML-Specific Chips


Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. To view part one, click here. Part two is here. SE: Is the industry's knowledge of machine learning keeping up with th... » read more

Where ML Works Best


Anirudh Devgan, president of Cadence, sat down with Semiconductor Engineering to discuss machine learning inside and outside of EDA tools and how that will affect the future of chip and system design. What follows are excerpts of that discussion. SE: How do you see the market and use of machine learning shaping up? Devgan: There are three main areas—machine learning inside, machine lear... » read more

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