In 2019, 3D imaging has become mainstream in mobile phone cameras. What started in 2016 with the first two smartphones using an Infineon 3D time-of-flight (ToF) imager has now become a major differentiator for mobile phone manufacturers. Today all leading […]
Magik-Eye is a global team of computer vision veterans that have developed a new method to determine depth from light directly without the need to measure time, find a complex pattern or perform complex computation. The Magik-Eye technique dramatically reduces […]
As robust depth cameras become more affordable, many new products will benefit from true 3D vision. This presentation will highlight the benefits of depth sensing for tasks such as autonomous navigation, collision avoidance and object detection in robots and drones. […]
Is it possible to deploy deep learning models on low-cost, low-power microcontrollers? While it may be surprising, the answer is a definite “yes”! In this talk, we’ll explain how the new TensorFlow Lite framework enables creating very lightweight DNN implementations […]
The Dynamically Reconfigurable Processing (DRP) block in the Arm Cortex®-A9 based RZ/A2M MPU accelerates image processing algorithms with spatially-pipelined, time-multiplexed reconfigurable-hardware compute resources. This hybrid ARM/DRP architecture combines the economy, flexibility and ease-of-use of microprocessors with the high throughput and […]
Even the most advanced AI chip architectures suffer from performance and energy efficiency limitations caused by the memory bottleneck between computing cores and data. Most state-of-the-art CPUs, GPUs, TPUs and other neural network hardware accelerators are limited by the latency, […]
Simultaneous localization and mapping (SLAM) technology has been evolving for quite some time, including visual SLAM, which relies primarily on image data. But implementing fast, accurate visual SLAM in embedded devices has been challenging due to high compute and precision […]
MediaTek has incorporated an AI processing unit (APU) alongside the traditional CPU and GPU in its SoC designs for the next wave of smart client devices (smartphones, cameras, appliances, cars, etc.). Edge applications can harness the CPU, GPU and APU […]
AI inference demands orders of magnitude more compute capacity than what today’s SoCs offer. At the same time, neural network topologies are changing too quickly to be addressed by ASICs that take years to go from architecture to production. In […]
AI inference at the edge will continue to create insatiable demand for compute performance in power- and cost-constrained form factors. Taking into account past trends, continuous scale-up of algorithms and the real economic value now being generated by AI at […]
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