Nvidia to Buy Mellanox for $6.9B


Nvidia reached a definitive agreement to acquire Mellanox Technologies for $125 a share in cash, giving the deal an enterprise value of about $6.9 billion. The proposed transaction would complement Nvidia’s product portfolio in high-performance computing for applications in artificial intelligence and big data analytics, with Mellanox’s specialty in providing interconnects for hyperscale da... » read more

Designing An AI SoC


Susheel Tadikonda, vice president of networking and storage at Synopsys, looks at how to achieve economies of scale in AI chips and where the common elements are across all the different architectures. https://youtu.be/fm0kxnj3DuM » read more

AI: Where’s The Money?


A one-time technology outcast, Artificial Intelligence (AI) has come a long way. Now there’s groundswell of interest and investments in products and technologies to deliver high performance visual recognition, matching or besting human skills. Equally, speech and audio recognition are becoming more common and we’re even starting to see more specialized applications such as finding optimized... » read more

The Other Side Of Makimoto’s Wave


Custom hardware is undergoing a huge resurgence across a variety of new applications, pushing the semiconductor industry to the other side of Makimoto's Wave. Tsugio Makimoto, the technologist who identified the chip industry’s 10-year cyclical swings between standardization and customization, predicted there always will be room in ASICs for general-purpose processors. But it's becoming mo... » read more

Designing Networking Chips


Susheel Tadikonda, vice president of networking and storage at Synopsys, talks about what’s changed in the way networking chips are being designed to deal with a massive increase in data. One of those shifts involves software-defined networking, where the greatest complexity resides in the software. That also has a big impact on the entire design flow, from pre-silicon to post-silicon. htt... » read more

The Data Deluge


Lip-Bu Tan, president and CEO of Cadence, sat down with Semiconductor Engineering to discuss the intersection of big data and technology, from the data center to the edge and vertical markets such as automotive. What follows are excerpts of that conversation. SE: What are the biggest changes you've seen over the past year? Tan: We are moving quickly toward data-driven economics. There... » 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

Dirty Data: Is the Sensor Malfunctioning?


Sensors provide an amazing connection to the physical world, but extracting usable data isn't so simple. In fact, many first-time IoT designers are unprepared for how messy a sensor’s data can be. Every day the IoT motion-sensor company MbientLab struggles to tactfully teach its customers that the mountain of data they are seeing is not because the sensors are faulty. Instead, the system d... » read more

VM Aware Fibre Channel Virtual Machine Traffic Visibility for SANs


Mysteries of the world, such as how the Monarch butterflies can find their way migration paths all the way back to their species’ origination point even though they had never been there before, but these occurrences in nature should remain a complete mystery. With VM clusters generating an increasing amount of FC traffic that crisscrosses across SANs within enterprise/datacenter ecosystem, th... » 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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