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Power Issues Grow For Cloud Chips


Performance levels in traditional or hyperscale data centers are being limited by power and heat caused by an increasing number of processors, memory, disk and operating systems within servers. The problem is so complex and intertwined, though, that solving it requires a series of steps that hopefully add up to a significant reduction across a system. But at 7nm and below, predicting exactly... » 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

Do Parallel Tools Make Sense?


Semiconductor Engineering sat down to talk about parallelization efforts within EDA with Andrea Casotto, chief scientist for Altair; Adam Sherer, product management group director in the System & Verification Group of Cadence; Harry Foster, chief scientist for Mentor, a Siemens Business; Vladislav Palfy, global manager for applications engineering at OneSpin; Vigyan Singhal, chief Oski for ... » read more

AI, ML Chip Choices


Flex Logix’s Cheng Wang talks about which types of chips work best for neural networks, AI and machine learning. https://youtu.be/k7OdP7B10o8 » 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

IBM Takes AI In Different Directions


Jeff Welser, vice president and lab director at IBM Research Almaden, sat down with Semiconductor Engineering to discuss what's changing in artificial intelligence and what challenges still remain. What follows are excerpts of that conversation. SE: What's changing in AI and why? Welser: The most interesting thing in AI right now is that we've moved from narrow AI, where we've proven you... » read more

Speeding Up High-Frequency Trading


The High-Frequency Trading (HFT) industry has received a lot of attention during the last few years. HFT is all about speed and minimizing latency: the faster you can run trading strategies and algorithms for analyzing minute price changes and executing trade orders, the higher the probability to win over competition. So the competition in this area is very fierce with market players continuous... » read more

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