What’s New At Hot Chips


By Jeff Dorsch & Ed Sperling Machine learning, artificial intelligence and neuromorphic computing took center stage at Hot Chips 2017 this week, a significant change from years past where the focus was on architectures that addressed improvements in speed and performance for standard compute problems. What is clear, given the focus of presentations, is that the bleeding edge of comput... » read more

Enabling Higher System Performance With NVDIMM-N


The shift from the traditional enterprise data center to the cloud is driving an insatiable demand for increased bandwidth and lower latencies. This is fundamentally reshaping traditional memory, storage, network and computing architectures. Although the semiconductor industry has been innovating to meet the needs of these new architectures, it continues to grapple with a waning Moore’s Law t... » read more

The Rising Value Of Data


The volume of data being generated by a spectrum of devices continues to skyrocket. Now the question is what can be done with that data. By Cisco's estimates, traffic on the Internet will be 3.3 zetabytes per year by 2021, up from 1.2 zetabytes in 2016. And if that isn't enough, the flow of data isn't consistent. Traffic on the busiest 60-minute period in a day increased 51% in 2016, compare... » read more

The Week In Review: IoT


Legislation Four senators plan to introduce a bipartisan bill that would require federal government vendors to provide Internet-connected devices and equipment that is patchable and conforms to industry cybersecurity standards. Such products must not have unchangeable passwords or known security vulnerabilities. The bill was drafted with expert advice from the Atlantic Council and Harvard Univ... » read more

Rethinking SSDs In Data Centers


Semiconductors that control how data gets on and off solid-state drives (SSDs) inside of data centers are having a moment in the sun. This surge in interest involves much more than just the SSD device. It leverages an entire ecosystem, starting with system architects and design engineers, who must figure out the best paths for data flow on- and off-chip and through a system. It also includes... » read more

What Does An IoT Chip Look Like?


By Ed Sperling and Jeff Dorsch Internet of Things chip design sounds like a simple topic on the face of it. Look deeper, though, and it becomes clear there is no single IoT, and certainly no type of chip that will work across the ever-expanding number of applications and markets that collectively make up the IoT. Included under this umbrella term are sensors, various types of processors, ... » read more

Cutting CapEx, Not Capacity


‘The cloud’ has been an industry buzz word for some time now, and while the initial focus was on data storage and sharing - and spawned the likes of Dropbox – ‘cloud computing’ is currently the latest trend. For instance, Amazon’s cloud platform, Amazon Web Services (AWS), gives users access to servers and a range of applications. Storage is available as before but so too now are... » read more

DAC 2017: A Glimpse Of How The Future Is Enabled


Last week’s Design Automation Conference in Austin gave great examples on how the future is enabled with next generation tools today. My favorite portions were Uhnder’s overview on “Agile Emulation” in the cloud, SirusXM’s presentation on how they used our portfolio of emulation and FPGA-based prototyping, the panel on “Smarter Verification” that I had organized and – of course ... » read more

Verification In The Cloud


By Ed Sperling Leasing of cloud-based verification resources on an as-needed basis is finally beginning to gain traction after more than a decade of false starts and over-optimistic expectations. All of the major EDA vendors now offer cloud-based services. They view this as a way of either supplementing a chipmaker's existing resources at various peak use times, or for small and midsize com... » read more

Deep Learning Robust Grasps with Synthetic Point Clouds & Analytic Grasp Metrics (UC Berkeley)


Source: The research was the work of Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg with support from the AUTOLAB team at UC Berkeley. Nimble-fingered robots enabled by deep learning Grabbing awkwardly shaped items that humans regularly pick up daily is not so easy for robots, as they don’t know where to apply grip... » read more

← Older posts