Canvas Category Software : Edge Computing : AI Inference
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.
Improving Image Resolution At The Edge
Manufacturing Shifts To AI Of Things
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 — say 10% — 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.
How To Measure ML Model Accuracy
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.
Flex Logix Raises $55M Series D Financing As It Accelerates Market Adoption of AI Inference and eFPGA Solutions
Flex Logix® 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 Logix’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 Logix’s InferX™ X1 edge inference accelerator which is now running YOLOv3 object detection and sampling to lead customers.
Edge-Inference Architectures Proliferate
What makes one AI system better than another depends on a lot of different factors, including some that aren’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.