tag: ML

Performance Metrics For Convolutional Neural Network Accelerators
By Vinay Mehta - 06 Aug, 2020 - Comments: 0

Across the industry, there are few benchmarks that customers and potential end users can employ to evaluate an inference acceleration solution end-to-end. Early on in this space, the performance of an accelerator was measured as a single number: TOPs. However, the limitations of using a single number has been covered in detail in the past by previous blogs. Nevertheless, if the method of cal... » read more

Designer And IP Tracks Swell With Focus On ML, Security And Traditional EDA Methodologies
By Michelle Clancy Fuller - 13 Jul, 2020 - Comments: 0

What are designers keenly interested in as the 57th Design Automation Conference (DAC) approaches? If you said machine learning (ML), you’d be only partially right. Based on designer and IP tracks submissions to the 57th edition of the venerable electronics-industry event, ML – how to design with it and optimize EDA tools and flows using it – is a hot topic. But so too are more traditi... » read more

Machine Learning Enabled Root Cause Analysis For Low Power Verification
By Himanshu Bhatt - 13 Jul, 2020 - Comments: 0

By Himanshu Bhatt and Susantha Wijesekara Next-generation SoCs with advanced graphics, computing and artificial intelligence capabilities are posing unforeseen challenges in verification. Designers and verification engineers using static verification technologies for low power often see many violations in the initial stages. Efficient debugging and determining root cause is a real issue and ... » read more

Are Better Machine Training Approaches Ahead?
By Bryon Moyer - 01 Jul, 2020 - Comments: 1

We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

ML Opening New Doors For FPGAs
By Bryon Moyer - 24 Jun, 2020 - Comments: 0

FPGAs have long been used in the early stages of any new digital technology, given their utility for prototyping and rapid evolution. But with machine learning, FPGAs are showing benefits beyond those of more conventional solutions. This opens up a hot new market for FPGAs, which traditionally have been hard to sustain in high-volume production due to pricing, and hard to use for battery-dri... » read more

3 Challenges In Edge Designs
By Ed Sperling - 15 Jun, 2020 - Comments: 0

As companies begin exploring what will be necessary to win at the edge, they are coming up with some daunting challenges. Designing chips for the edge is far different than for the IoT/IIoT. The idea with the IoT was that simple sensors would relay data through a gateway to the cloud, where it would be processed and data could be sent back to the device as needed. That works if it's a small ... » read more

Week In Review: Auto, Security, Pervasive Computing
By SE Staff - 12 Jun, 2020 - Comments: 0

Edge, cloud, data center Programmable logic company Efinix used Cadence’s Digital Full Flow to finish Efinix’s Trion FPGA family for edge computing, AI/ML and vision processing applications, according to a press release. Last week Efinix also announced three software defined SoCs based on the RISC-V core. The SoCs are optimized to the Trion FPGAs. AI, machine learning Amazon will tempo... » read more

Hardware Security For AI Accelerators
By Rambus - 03 Jun, 2020 - Comments: 0

Dedicated accelerator hardware for artificial intelligence and machine learning (AI/ML) algorithms are increasingly prevalent in data centers and endpoint devices. These accelerators handle valuable data and models, and face a growing threat landscape putting AI/ML assets at risk. Using fundamental cryptographic security techniques performed by a hardware root of trust can safeguard these as... » read more

Powering The Edge
By Arm - 13 May, 2020 - Comments: 0

On-device machine learning (ML) is a phenomenon that has exploded in popularity. Smart devices that are able to make independent decisions, acting on locally generated data, are hailed as the future of compute for consumer devices: on-device processing slashes latency; increases reliability and safety; boosts privacy and security...all while saving on power and cost. Although ML in edge d... » read more

What Is DRAM’s Future?
By Bryon Moyer - 09 Apr, 2020 - Comments: 0

Memory — and DRAM in particular — has moved into the spotlight as it finds itself in the critical path to greater system performance. This isn't the first time DRAM has been the center of attention involving performance. The problem is that not everything progresses at the same rate, creating serial bottlenecks in everything from processor performance to transistor design, and even the t... » read more

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