Naveen Suda
Naveen Suda
Verified email at
Cited by
Cited by
Federated Learning with Non-IID Data
Y Zhao, M Li, L Lai, N Suda, D Civin, V Chandra
arXiv preprint arXiv:1806.00582, 2018
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks
N Suda, V Chandra, G Dasika, A Mohanty, Y Ma, S Vrudhula, J Seo, ...
Proceedings of the 2016 ACM/SIGDA International Symposium on Field …, 2016
Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network
H Sharma, J Park, N Suda, L Lai, B Chau, JK Kim, V Chandra, ...
2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture …, 2018
Hello Edge: Keyword Spotting on Microcontrollers
Y Zhang, N Suda, L Lai, V Chandra
arXiv preprint arXiv:1711.07128, 2017
CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
L Lai, N Suda, V Chandra
arXiv preprint arXiv:1801.06601, 2018
Scalable and modularized RTL compilation of convolutional neural networks onto FPGA
Y Ma, N Suda, Y Cao, J Seo, S Vrudhula
2016 26th International Conference on Field Programmable Logic and …, 2016
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
L Lai, N Suda, V Chandra
arXiv preprint arXiv:1703.03073, 2017
ALAMO: FPGA acceleration of deep learning algorithms with a modularized RTL compiler
Y Ma, N Suda, Y Cao, S Vrudhula, J Seo
Integration 62, 14-23, 2018
Enabling Deep Learning at the loT Edge
L Lai, N Suda
2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 1-6, 2018
Dream Distillation: A Data-Independent Model Compression Framework
K Bhardwaj, N Suda, R Marculescu
arXiv preprint arXiv:1905.07072, 2019
SenseHAR: a robust virtual activity sensor for smartphones and wearables
JV Jeyakumar, L Lai, N Suda, M Srivastava
Proceedings of the 17th Conference on Embedded Networked Sensor Systems, 15-28, 2019
A Low-Noise Output Capacitorless Low-Dropout Regulator With a Switched-RC Bandgap Reference
R Magod, N Suda, V Ivanov, R Balasingam, B Bakkaloglu
IEEE Transactions on Power Electronics 32 (4), 2856-2864, 2017
Privynet: A flexible framework for privacy-preserving deep neural network training
M Li, L Lai, N Suda, V Chandra, DZ Pan
arXiv preprint arXiv:1709.06161, 2017
Collapsible linear blocks for super-efficient super resolution
K Bhardwaj, M Milosavljevic, L O'Neil, D Gope, R Matas, A Chalfin, ...
Proceedings of Machine Learning and Systems 4, 529-547, 2022
Not All Ops Are Created Equal!
L Lai, N Suda, V Chandra
arXiv preprint arXiv:1801.04326, 2018
EdgeAl: A Vision for Deep Learning in the IoT Era
K Bhardwaj, N Suda, R Marculescu
IEEE Design & Test 38 (4), 37-43, 2019
Machine Learning on ARM Cortex-M Microcontrollers
N Suda, D Loh
Arm Ltd.: Cambridge, UK, 2019
A 65 nm Programmable ANalog Device Array (PANDA) for Analog Circuit Emulation
N Suda, J Suh, N Hakim, Y Cao, B Bakkaloglu
IEEE Transactions on Circuits and Systems I: Regular Papers 63 (2), 181-190, 2016
A 0.5-V low power analog front-end for heart-rate detector
N Suda, PV Nishanth, D Basak, D Sharma, RP Paily
Analog Integrated Circuits and Signal Processing 81, 417-430, 2014
Rethinking Machine Learning Development and Deployment for Edge Devices
L Lai, N Suda
arXiv preprint arXiv:1806.07846, 2018
The system can't perform the operation now. Try again later.
Articles 1–20