Week in Review: IoT, Security, Auto


Cybersecurity Check Point Software Technologies reports that facsimile machines (yes, people still use them!) can be subject to hacking through vulnerabilities in their communication protocols. The HP Officejet Pro All-in-One fax printers and other fax machines can be compromised with a hacker only knowing a fax number, according to the company. Check Point Research says a design flaw in Andro... » read more

High-Performance Memory At Low Cost Per Bit


Hardware developers of deep learning neural networks (DNN) have a universal complaint – they need more and more memory capacity with high performance, low cost and low power. As artificial intelligence (AI) techniques gain wider adoption, their complexity and training requirements also increase. Large and complex DNN models do not fit on the small on-chip SRAM caches near the processor. This ... » read more

Impact Of IP On AI SoCs


The combination of mathematics and processing capability has set in motion a new generation of technology advancements with an entire new world of possibilities related to Artificial Intelligence. AI mimics human behavior using deep learning algorithms. Neural networks are what we define as deep learning, which is a subset of machine learning, which is yet a subset of AI, as shown in Figure 1. ... » read more

IoT Meets ML


AI and machine learning are the next big things, and they're going make a huge difference in the adoption and capabilities of the IoT. Unlike previous technology approaches, AI, machine learning and deep learning are based on patterns. In effect, they raise up the level of abstraction for data. An image of a cat can be megabytes of data, and a cat taken from all angles may be gigabytes of da... » read more

Artificial Intelligence Chips: Past, Present and Future


Artificial Intelligence (AI) is much in the news these days. AI is making medical diagnoses, synthesizing new chemicals, identifying the faces of criminals in a huge crowd, driving cars, and even creating new works of art. Sometimes it seems as if there is nothing that AI cannot do and that we will all soon be out of our jobs, watching the AIs do everything for us. To understand the origins ... » read more

Faster Verification With AI, ML


Tool providers have continually improved the performance, capacity, and memory footprint parameters of functional verification engines over the past decade. Today, although the core anchors are still formal verification, simulation, emulation, and FPGA-based prototyping, a new frontier focusing on the verification fabric itself aims to make better use of these engines including planning, alloca... » 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

Fabs Meet Machine Learning


Aki Fujimura, chief executive of D2S, sat down with Semiconductor Engineering to discuss Moore’s Law and photomask technology. Fujimura also explained how artificial intelligence and machine learning are impacting the IC industry. What follows are excerpts of that conversation. SE: For some time, you’ve said we need more compute power. So we need faster chips at advanced nodes, but cost... » read more

Architecting For AI


Semiconductor Engineering sat down to talk about what is needed today to enable artificial intelligence training and inferencing with Manoj Roge, vice president, strategic planning at Achronix; Ty Garibay, CTO at Arteris IP; Chris Rowen, CEO of Babblelabs; David White, distinguished engineer at Cadence; Cheng Wang, senior VP engineering at Flex Logix; and Raik Brinkmann, president and CEO of O... » read more

Security Holes In Machine Learning And AI


Machine learning and AI developers are starting to examine the integrity of training data, which in some cases will be used to train millions or even billions of devices. But this is the beginning of what will become a mammoth effort, because today no one is quite sure how that training data can be corrupted, or what to do about it if it is corrupted. Machine learning, deep learning and arti... » read more

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