tinyML Research Symposium: Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR...
Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications
Manan SURI , Leader of NVM and Neuromorphic Hardware Research group, Indian Institute of Technology Delhi
Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. In this work, we investigate two representative XR workloads: (i) Hand detection and (ii) Eye segmentation, for hardware design space exploration. For both applications, we train deep neural networks and analyze the impact of quantization and hardware specific bottlenecks. Through simulations, we evaluate a CPU and two systolic inference accelerator implementations. Next, we compare these hardware solutions with advanced technology nodes. The impact of integrating state-of-the-art emerging non-volatile memory technology (STT/SOT/VGSOT MRAM) into the XR-AI inference pipeline is evaluated. We found that significant energy benefits (≥24%) can be achieved for hand detection (IPS=10) and eye segmentation (IPS=0.1) by introducing non-volatile memory in the memory hierarchy for designs at 7nm node while meeting minimum IPS (inference per second). Moreover, we can realize substantial reduction in area (≥30%) owing to the small form factor of MRAM compared to traditional SRAM.
Видео tinyML Research Symposium: Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR... канала The tinyML Foundation
Manan SURI , Leader of NVM and Neuromorphic Hardware Research group, Indian Institute of Technology Delhi
Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. In this work, we investigate two representative XR workloads: (i) Hand detection and (ii) Eye segmentation, for hardware design space exploration. For both applications, we train deep neural networks and analyze the impact of quantization and hardware specific bottlenecks. Through simulations, we evaluate a CPU and two systolic inference accelerator implementations. Next, we compare these hardware solutions with advanced technology nodes. The impact of integrating state-of-the-art emerging non-volatile memory technology (STT/SOT/VGSOT MRAM) into the XR-AI inference pipeline is evaluated. We found that significant energy benefits (≥24%) can be achieved for hand detection (IPS=10) and eye segmentation (IPS=0.1) by introducing non-volatile memory in the memory hierarchy for designs at 7nm node while meeting minimum IPS (inference per second). Moreover, we can realize substantial reduction in area (≥30%) owing to the small form factor of MRAM compared to traditional SRAM.
Видео tinyML Research Symposium: Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR... канала The tinyML Foundation
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![tinyML Talks Taiwan in Mandarin and English: Discovering tinyML](https://i.ytimg.com/vi/hPtRLygc4LE/default.jpg)
![tinyML Summit 2022: Sensing Applications as a Driver for TinyML Solutions](https://i.ytimg.com/vi/pUAAGVVYgLQ/default.jpg)
![tinyML Neuromorphic Engineering Forum - Sensors Session](https://i.ytimg.com/vi/Mt9X0ALdCpI/default.jpg)
![tinyML Vision Challenge - Himax & Edge Impulse](https://i.ytimg.com/vi/6tmCEzNSIas/default.jpg)
![tinyML Talks Chao Xu: Enabling Neural network at the low power edge: A neural network compiler...](https://i.ytimg.com/vi/Rs1yeTSuZLA/default.jpg)
![SensMACH 2020 Daniel Situnayake: Embedded machine learning in the real world](https://i.ytimg.com/vi/a67hWPT1NLE/default.jpg)
![tinyML Talks: Empowering the Edge: Practical Applications of Embedded Machine Learning on MCUs](https://i.ytimg.com/vi/tkqNS611cRc/default.jpg)
![tinyML Talks: Efficient AI for Wildlife Conservation](https://i.ytimg.com/vi/FfvcZEMn2l0/default.jpg)
![tinyML Research Symposium 2022: Towards Agile Design of Neural Processing Units with Chisel](https://i.ytimg.com/vi/xlP1xdKRrqc/default.jpg)
![tinyML Talks Phoenix: Novel Device and Materials in Emerging Memory for Neuromorphic Computing](https://i.ytimg.com/vi/_apkQF1ZL6A/default.jpg)
![tinyML Talks - Phoenix meetup: Analog TinyML for health management using intelligent wearables](https://i.ytimg.com/vi/bCzg8y6aRi8/default.jpg)
![tinyML Talks India: Single Lead ECG Classification On Wearable and Implantable Devices](https://i.ytimg.com/vi/uHywaYleCtA/default.jpg)
![tinyML Summit 2023:Personal Computing devices use-case and applications enabled by Smart Sensors](https://i.ytimg.com/vi/9hvz6ZB5G8A/default.jpg)
![tinyML Talks: From the lab to the edge: Post-Training Compression](https://i.ytimg.com/vi/Ada9Tq8JAX8/default.jpg)
![tinyML Talks: State of Hardware & Software Ecosystem for Low-Power ML Applications on RISC-V](https://i.ytimg.com/vi/Rcbrc2rnXlk/default.jpg)
![tinyML Talks: Meetup Italy with small-medium industries](https://i.ytimg.com/vi/sAmRSm-tdd4/default.jpg)
![tinyML Hackathon Challenge 2023 - Infineon XENSIV 60GHz Radar Sensor and devkit explanation](https://i.ytimg.com/vi/yL6f61MKzFo/default.jpg)
![tinyML Auto ML Tutorial with Qeexo](https://i.ytimg.com/vi/qo0JTM6gaIE/default.jpg)
![tinyML On Device Learning Forum - Warren Gross: On-Device Learning For Natural Language Processing..](https://i.ytimg.com/vi/ERLFRluwRjA/default.jpg)
![EMEA 2021 tiny Talks: Building Heterogeneous TinyML Pipelines](https://i.ytimg.com/vi/p-Rtnvj4L4I/default.jpg)
![tinyML EMEA 2022- Eran Treister: Wavelet Feature Maps Compression for Image-to-Image CNNs](https://i.ytimg.com/vi/fPmvwecx7TY/default.jpg)