Flex Logix

Canvas Category Software : Edge Computing : AI Inference


Primary Location Mountain View, California, United States

Financial Status VC-D; Mithril Capital Management, Lux Capital, Eclipse Ventures, Tate Family Trust

Leading provider of eFPGA IP and software for flexible, accelerated chips & now nnMAX IP and InferX co-processor for high inference throughput at low power/cost.

Assembly Line

Improving Image Resolution At The Edge

Manufacturing Shifts To AI Of Things

๐Ÿ“… Date:

โœ๏ธ Author: John Koon

๐Ÿ”– Topics: IIoT, edge computing

๐Ÿข Organizations: Infineon, Flex Logix, Silicon Labs

Preventive maintenance is an important part of smart manufacturing, but this is just the beginning. AIoT can be deployed in many different areas in a factory to further increase productivity. For example, it can be used for incoming inspection. Traditionally, the quality control department performs sample inspection. Instead of inspecting 100% of the components used to build a device, only a sample โ€” say 10% โ€” will be audited. With the installation of a 3D HD camera, AIoT can inspect 100% of the components and screen out defective parts at an early stage. Additionally, a robotic arm can pick out defective components or those of different colors and/or shapes, further reducing reject rates.

AIoT also can be used to improve worker safety, resulting in lower worker compensation payments. For example, a warehouse can be equipped with AIoT cameras to ensure only authorized workers wearing appropriate safety equipment can enter the warehouse.

Read more at Semiconductor Engineering

How To Measure ML Model Accuracy

๐Ÿ“… Date:

โœ๏ธ Author: Bryon Moyer

๐Ÿ”– Topics: machine learning

๐Ÿข Organizations: Ansys, Brainome, Cadence, Flex Logix, Synopsys, Xilinx

Machine learning (ML) is about making predictions about new data based on old data. The quality of any machine-learning algorithm is ultimately determined by the quality of those predictions.

However, there is no one universal way to measure that quality across all ML applications, and that has broad implications for the value and usefulness of machine learning.

Read more at Semiconductor Engineering

Flex Logix Raises $55M Series D Financing As It Accelerates Market Adoption of AI Inference and eFPGA Solutions

๐Ÿ“… Date:

๐Ÿ”– Topics: FPGA, funding event

๐Ÿข Organizations: Flex Logix

Flex Logixยฎ Technologies, Inc., supplier of the fastest and most-efficient AI edge inference accelerator and the leading supplier of eFPGA IP, announced today the close of a $55 million oversubscribed Series D funding round. Mithril Capital Management led the financing with significant participation by existing investors Lux Capital, Eclipse Ventures and the Tate Family Trust.

Flex Logixโ€™s inference architecture is unique. It is optimized for low latency operation required by edge megapixel vision applications. It combines numerous 1-dimensional tensor processors with reconfigurable, high bandwidth, non-blocking interconnect that enables each layer of the neural network model to be configured for maximum utilization, resulting in very high performance with less cost and power. The connections between compute and memory are reconfigured in millionths of a second as the model is processed. This architecture is the basis of Flex Logixโ€™s InferXโ„ข X1 edge inference accelerator which is now running YOLOv3 object detection and sampling to lead customers.

Read more at PR Newswire

Edge-Inference Architectures Proliferate

๐Ÿ“… Date:

โœ๏ธ Author: Bryon Moyer

๐Ÿ”– Topics: AI, machine learning, edge computing

๐Ÿญ Vertical: Semiconductor

๐Ÿข Organizations: Cadence, Hailo, Google, Flex Logix, BrainChip, Synopsys, GrAI Matter, Deep Vision, Maxim Integrated

What makes one AI system better than another depends on a lot of different factors, including some that arenโ€™t entirely clear.

The new offerings exhibit a wide range of structure, technology, and optimization goals. All must be gentle on power, but some target wired devices while others target battery-powered devices, giving different power/performance targets. While no single architecture is expected to solve every problem, the industry is in a phase of proliferation, not consolidation. It will be a while before the dust settles on the preferred architectures.

Read more at Semiconductor Engineering