Nightmare Fuel: The Hazards Of ML Hardware Accelerators


A major design challenge facing numerous silicon design teams in 2023 is building the right amount of machine learning (ML) performance capability into today’s silicon tape out in anticipation of what the state of the art (SOTA) ML inference models will look like in 2026 and beyond when that silicon will be used in devices in volume production. Given the continuing rapid rate of change in mac... » read more

Identifying PCB Defects with a Deep Learning Single-Step Detection Model


This new technical paper titled "End-to-end deep learning framework for printed circuit board manufacturing defect classification" is from researchers at École de technologie supérieure (ÉTS) in Montreal, Quebec. Abstract "We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturi... » read more

New Ways To Optimize Machine Learning


As more designers employ machine learning (ML) in their systems, they’re moving from simply getting the application to work to optimizing the power and performance of their implementations. Some techniques are available today. Others will take time to percolate through the design flow and tools before they become readily available to mainstream designers. Any new technology follows a basic... » read more

Software In Inference Accelerators


Geoff Tate, CEO of Flex Logix, talks about the importance of hardware-software co-design for inference accelerators, how that affects performance and power, and what new approaches chipmakers are taking to bring AI chips to market. » read more

Inferencing Efficiency


Geoff Tate, CEO of Flex Logix, talks with Semiconductor Engineering about how to measure efficiency in inferencing chips, how to achieve the most throughput for the lowest cost, and what the benchmarks really show. » read more

Benchmarks For The Edge


Geoff Tate, CEO of Flex Logix, talks about benchmarking in edge devices, particularly for convolutional neural networks. https://youtu.be/-beVEpKAM4M » read more