The Winograd Transformation


Cheng Wang, senior vice president of engineering at Flex Logix, explains how the Winograd Transformation applies to convolutional neural networks. https://youtu.be/E7QJUby9x-I » read more

Self-Driving Architecture With eFPGAs


The favored self-driving architecture of the future will be increasingly decentralized. However, both the centralized and decentralized architectural design approaches will require hardware acceleration in the form of far more lookaside co-processing than is currently realized. Whether centralized or decentralized, the anticipated computing architectures for automated and autonomous driving ... » read more

Mine Cryptocurrencies Sooner, Faster, and Cheaper with Achronix Speedcore Embedded FPGAs


New cryptocurrencies such as Monero introduce ASIC-resistance and memory-hardness to prevent ASICs from being built that give some operators a competitive mining advantage over others who do not have access to the same technology. This white paper discusses the relevant background and presents a solution based on Achronix Speedcore embedded FPGAs (eFPGAs), enabling users to regain a highly prof... » read more

EDA Grabs Bigger Slice Of Chip Market


EDA revenues have been a fairly constant percentage of semiconductor revenues, but that may change in 2019. With new customers creating demand, and some traditional customers shifting focus from advanced nodes, the various branches of the EDA tool industry may be where sticky technical problems are solved. IC manufacturing, packaging and development tools all are finding new ways to handle t... » read more

Mostly Upbeat Outlook For Chips


2019 has started with cautious optimism for the semiconductor industry, despite dark clouds that dot the horizon. Market segments such as cryptocurrencies and virtual reality are not living up to expectations, the market for smart phones appears to be saturated, and DRAM prices are dropping, leading to cut-backs in capital expenditures. EDA companies are talking about sales to China being pu... » read more

What Makes A Chip Design Successful Today?


"Transistors are free" was the rallying cry of the semiconductor industry during the 1990s and early 2000s. That is no longer true. The end of Dennard scaling made the simultaneous use of all the transistors troublesome, but transistors remained effectively unlimited. This led to an era where large amounts of flexibility could be built into a chip. It didn't matter if all of it was being use... » read more

Week In Review: Design, Low Power


RISC-V Western Digital announced big plans for RISC-V with a new open source RISC-V core, an open standard initiative for cache coherent memory over a network, and an open source RISC-V instruction set simulator. The SweRV Core features a 2-way superscalar design with a 32-bit, 9 stage pipeline core. It has clock speeds of up to 1.8Ghz on a 28mm CMOS process technology and will be used in vari... » read more

eFPGAs Offer Practical Solution For Embedded Vision Applications


Video applications, such as surveillance, object detection and motion analysis, rely on 360° embedded vision and high-resolution fish-eye cameras lenses with a wide-angle field of view (FOV). These systems have up to six real-time camera streams processing together frame by frame. Each frame is corrected for distortion and other image artifacts, adjusted for exposure and white balance, then st... » read more

Inferencing In Hardware


Cheng Wang, senior vice president of engineering at Flex Logix, examines shifting neural network models, how many multiply-accumulates are needed for different applications, and why programmable neural inferencing will be required for years to come. https://youtu.be/jb7qYU2nhoo         See other tech talk videos here. » read more

AI Chip Architectures Race To The Edge


As machine-learning apps start showing up in endpoint devices and along the network edge of the IoT, the accelerators that make AI possible may look more like FPGA and SoC modules than current data-center-bound chips from Intel or Nvidia. Artificial intelligence and machine learning need powerful chips for computing answers (inference) from large data sets (training). Most AI chips—both tr... » read more

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