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  6. Edge Device Implementation - Artificial Intelligence (AI) Development

Edge Device Implementation - Artificial Intelligence (AI) Development

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last updated:Mar 11, 2022

三栄ハイテックス
三栄ハイテックス
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Design an optimal processing architecture for the target hardware from the trained algorithms, enabling fast execution on edge devices.

By simply providing the algorithm developed by the customer, we will interpret its content and devise an architecture suitable for the algorithm. We support various frameworks such as Caffe, Keras, TensorFlow, and PyTorch. Drawing on decades of experience in digital circuit design and embedded software development, we will realize optimal solutions without burdening the customer. To implement algorithms on devices such as FPGAs and GPUs in edge environments, it is necessary to have a thorough understanding of the target device's characteristics and resources, which may require architectural design and, in some cases, network optimization. We leverage our implementation experience on devices including FPGAs, GPUs (Jetson AGX Xavier), and AI chips such as TPUs (Google TPU) and VPUs (Intel Movidius Myriad X VPU) to meet the customer's needs from device selection to implementation. <Achievements> ○ Pose estimation ○ General object detection ○ In-vehicle area segmentation

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Edge Device Implementation - Artificial Intelligence (AI) Development

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  • Related Link - https://www.sanei-hy.co.jp/service/ai/edge-device/

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To implement algorithms on devices such as FPGAs and GPUs in edge environments, it is necessary to have a thorough understanding of the characteristics and resources of the target devices, as well as to design the architecture and, in some cases, optimize the network. Our company leverages our implementation experience with FPGAs, GPUs (Jetson AGX Xavier), and AI chips such as TPUs (Google TPU) and VPUs (Intel Movidius Myriad X VPU) to meet our customers' needs from device selection to implementation.

Applications/Examples of results

○ Pose Estimation Implemented customer development algorithms on Zynq and developed a demo system for end users. ○ General Object Detection Developed the implementation environment using SDSoC (Xilinx high-level synthesis tool) and performed initial implementation on Zynq. ○ In-vehicle Area Segmentation Considered deploying segmentation algorithms targeting FPGA, TPU, and VPU.

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Sanei Hitec Co., Ltd. Company Profile

Sanei Hitec Co., Ltd. Company Profile

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三栄ハイテックス

三栄ハイテックス

Electronic Components and Semiconductors

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Our company has built a track record as a professional group in LSI design, distinguishing ourselves as an independent design specialist firm by having an LSI design environment equivalent to that of our clients and being able to handle the entire process of LSI design, from circuit design to layout and verification, for both analog and digital. Over the past half-century since our founding, the environment surrounding the LSI industry has continued to change, with semiconductor needs constantly expanding across various fields such as home appliances, musical instruments, gaming consoles, smartphones, and automobiles. With the advent of the IoT era, we believe this change will accelerate even further. Therefore, we have established overseas subsidiaries to enhance our responsiveness with a view to global expansion. Furthermore, by maximizing the strengths of our professional group, where 90% of our employees are engineers, we are continuing our efforts toward the future, including a full-scale entry into artificial intelligence (AI) as a new pillar of our engineering business and promoting joint technology development with academia and government, aiming for further evolution as a company that can meet societal expectations and fulfill its responsibilities.

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