Week In Review: Design, Low Power


Tools Synopsys introduced a new model for using its EDA tools on the cloud. Synopsys Cloud provides pay-as-you-go access to the company's cloud-optimized design and verification products, with pre-optimized infrastructure on Microsoft Azure to address higher levels of interdependencies in chip development. "As more design flows incorporate AI, requiring even more resources, the virtually unlim... » read more

Week In Review: Auto, Security, Pervasive Computing


Automotive Verizon and Cisco demonstrated a C-V2X network for autonomous driving in Las Vegas that avoids using costly physical roadside units to extend radio signals. Instead, Verizon and Cisco say their test proved that Verizon’s LTE network and public 5G Edge with AWS Wavelength, together with Cisco Catalyst IR1101 routers in connected infrastructure, were adequate to meet the latency nee... » read more

Week In Review: Design, Low Power


Intellectual Property Flex Logix inked an agreement with the Air Force Research Laboratory, Sensors Directorate (AFRL/RY) covering any Flex Logix IP technology for use in all US Government-funded programs for research and prototyping purposes with no license fees. “Our first license with AFRL for EFLX eFPGA in GlobalFoundries 12nm process was highly successful, with more than a half dozen pr... » read more

Machine Learning Showing Up As Silicon IP


New machine-learning (ML) architectures continue to appear. Up to now, each new offering has been implemented in a chip for sale, to be placed alongside host processors, memory, and other chips on an accelerator board. But over time, more of this technology could be sold as IP that can be integrated into a system-on-chip (SoC). That trend is evident at recent conferences, where an increasing... » read more

Why RISC-V Is Succeeding


There is no disputing the excitement surround the introduction of the RISC-V processor architecture. Yet while many have called it a harbinger of a much broader open-source hardware movement, the reasons behind its success are not obvious, and the implications for an expansion of more open-source cores is far from certain. “The adoption of RISC-V as the preferred architecture for many sili... » read more

Week In Review: Design, Low Power


Nvidia's proposed acquisition of Arm is officially off. The deal faced significant pushback from regulatory agencies in the UK, USA, and Europe, which feared it would reduce or limit competition in areas like data center. Nvidia indicated it would continue working with Arm, and it will retain a 20-year Arm license. (SoftBank will retain the $1.25 billion prepaid by Nvidia.) SoftBank said it wil... » read more

ML Focus Shifting Toward Software


New machine-learning (ML) architectures continue to garner a huge amount of attention as the race continues to provide the most effective acceleration architectures for the cloud and the edge, but attention is starting to shift from the hardware to the software tools. The big question now is whether a software abstraction eventually will win out over hardware details in determining who the f... » read more

Power Grids Under Attack


Cyberattacks are becoming as troublesome to the electrical power grid as natural disasters, and the problem is growing worse as these grids become more connected and smarter. Unlike in the past, when a power outage affected just the electricity supplied to homes and businesses, power grids are becoming core elements of smart cities, infrastructure, and safety-related services. Without power,... » read more

eFPGA Saved Us Millions of Dollars. It Can Do the Same for You


For those of you who follow Flex Logix, you already know that we have an IP business, EFLX eFGPA, and an edge inferencing co-processor chip and board business, InferX. InferX came about because we had many customers ask if they can run AI/ML algorithms in EFLX. The answer was and still is, of course you can – EFLX is an FPGA fabric similar to what FPGA chips use. Our co-founder, Cheng Wang, t... » read more

How Inferencing Differs From Training in Machine Learning Applications


Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train a model to enable the machine to learn how to perform a task. ML training is highly iterative with each new piece of training data generating trillions of operations. The iterative nature of the tr... » read more

← Older posts Newer posts →