no code implementations • 18 Nov 2024 • Igor Fedorov, Kate Plawiak, Lemeng Wu, Tarek Elgamal, Naveen Suda, Eric Smith, Hongyuan Zhan, Jianfeng Chi, Yuriy Hulovatyy, Kimish Patel, Zechun Liu, Changsheng Zhao, Yangyang Shi, Tijmen Blankevoort, Mahesh Pasupuleti, Bilge Soran, Zacharie Delpierre Coudert, Rachad Alao, Raghuraman Krishnamoorthi, Vikas Chandra
This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024.
no code implementations • 20 Jan 2024 • Junxiao Shen, Matthias De Lange, Xuhai "Orson" Xu, Enmin Zhou, Ran Tan, Naveen Suda, Maciej Lazarewicz, Per Ola Kristensson, Amy Karlson, Evan Strasnick
Unfortunately, in real-world applications involving gesture recognition, such as gesture recognition based on wrist-worn devices, the data distribution may change over time.
3 code implementations • 17 Mar 2021 • Kartikeya Bhardwaj, Milos Milosavljevic, Liam O'Neil, Dibakar Gope, Ramon Matas, Alex Chalfin, Naveen Suda, Lingchuan Meng, Danny Loh
Our results highlight the challenges faced by super resolution on AI accelerators and demonstrate that SESR is significantly faster (e. g., 6x-8x higher FPS) than existing models on mobile-NPU.
no code implementations • 23 Oct 2019 • Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices.
no code implementations • 17 May 2019 • Kartikeya Bhardwaj, Naveen Suda, Radu Marculescu
Model compression is eminently suited for deploying deep learning on IoT-devices.
no code implementations • 20 Jun 2018 • Liangzhen Lai, Naveen Suda
Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks.
2 code implementations • 2 Jun 2018 • Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra
Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.
1 code implementation • 19 Jan 2018 • Liangzhen Lai, Naveen Suda, Vikas Chandra
Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication.
no code implementations • 12 Jan 2018 • Liangzhen Lai, Naveen Suda, Vikas Chandra
Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices.
no code implementations • 5 Dec 2017 • Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Joon Kyung Kim, Vikas Chandra, Hadi Esmaeilzadeh
Compared to Stripes, BitFusion provides 2. 6x speedup and 3. 9x energy reduction at 45 nm node when BitFusion area and frequency are set to those of Stripes.
18 code implementations • 20 Nov 2017 • Yundong Zhang, Naveen Suda, Liangzhen Lai, Vikas Chandra
We train various neural network architectures for keyword spotting published in literature to compare their accuracy and memory/compute requirements.
Ranked #14 on
Keyword Spotting
on Google Speech Commands
no code implementations • ICLR 2018 • Meng Li, Liangzhen Lai, Naveen Suda, Vikas Chandra, David Z. Pan
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints.
no code implementations • 8 Mar 2017 • Liangzhen Lai, Naveen Suda, Vikas Chandra
To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations.