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

Week in Review: IoT, Security, Auto


Deals SoftBank Corp. reached an agreement with Indonesia’s Link Net to work together on Internet of Things technology. Hidebumi Kitahara of SoftBank said in a statement, “The global mobile industry is now entering the 5G era, with IoT becoming the central focal point of innovation. This partnership with Link Net shows our strong commitment to further boost technology innovation in the glob... » read more

System Bits: July 3


Machine learning network for personalized autism therapy MIT Media Lab researchers have developed a personalized deep learning network for therapy use with children with autism spectrum conditions. They reminded these children often have trouble recognizing the emotional states of people around them, such as distinguishing a happy face from a fearful face. To help with this, some therapists... » read more

Syntiant: Analog Deep Learning Chips


Startup Syntiant Corp. is an Irvine, Calif. semiconductor company led by former top Broadcom engineers with experience in both innovative design and in producing chips designed to be produced in the billions, according to company CEO Kurt Busch. The chip they’ll be building is an inference accelerator designed to run deep-learning processes 50x more efficiently than traditional stored-prog... » read more

System Bits: June 12


Writing complex ML/DL analytics algorithms Rice University researchers in the DARPA-funded Pliny Project believe they have the answer for every stressed-out systems programmer who has struggled to implement complex objects and workflows on ‘big data’ platforms like Spark and thought: “Isn’t there a better way?” Their answer: Yes with PlinyCompute, which the team describes as “a sys... » 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

CEO Outlook On Chip Industry


Semiconductor Engineering sat down with Wally Rhines, president and CEO of Mentor, a Siemens Business; Simon Segars, CEO of Arm; Grant Pierce, CEO of Sonics; and Dean Drako, CEO of IC Manage. What follows are excerpts of that conversation. To view part one, click here. L-R: Dean Drako, Grant Pierce, Wally Rhines, Simon Segars. Photo: Paul Cohen/ESD Alliance SE: AI, deep learning and mac... » read more

New Deep Learning Processors, Embedded FPGA Technologies, SoC Design Solutions


Some of the most valuable events at DAC are the IP Track sessions, which give small and midsize companies a chance to share innovations that might not get much attention elsewhere. The use of IP in SoCs has exploded in recent years. In a panel at DAC 2017, an industry expert noted that the IP market clearly was growing even faster than EDA itself, due to the fact that more and more chip mak... » read more

Deep Learning Neural Networks Drive Demands On Memory Bandwidth


A deep neural network (DNN) is a system that is designed similar to our current understanding of biological neural networks in the brain. DNNs are finding use in many applications, advancing at a fast pace, pushing the limits of existing silicon, and impacting the design of new computing architectures. Figure 1 shows a very basic form of neural network that has several nodes in each layer that ... » read more

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