Abhinav Sharma Email & Phone Number
Who is Abhinav Sharma? Overview
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Abhinav Sharma is listed as Efficient ML @ AMD | Ex - Nokia Bell Labs | MS CS @ Stony Brook University at AMD, a with 16705 employees, based in San Jose, California, United States. AeroLeads shows a matched LinkedIn profile for Abhinav Sharma.
Abhinav Sharma previously worked as Deep Learning Compiler Engineer 2 at Amd and ML Systems Research Co-op at Nokia Bell Labs. Abhinav Sharma holds Master'S Degree, Computer Science from Stony Brook University.
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About Abhinav Sharma
Hello! I'm Abhinav SharmaI specialize in Neural Network optimizations, my research focuses on accelerating inference, minimizing memory usage, and conserving energy by reducing FLOPs/MACs. I design systems to maximize resource utilization and allocation, catering to Edge Systems, Autonomous Vehicles, and Robotics.
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Abhinav Sharma work experience
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Ml Systems Research Co-Op
Ml Research Co-Op
1. Exploring how scale of an image can be used to determine optimal complexity of a Deep Neural Network.
Ml Research Intern
1. Designed an innovative approach using Inter-Class Similarity Metrics to optimize the size of Neural Network architecture. This approach compared similarities between different image class groups, ensuring enhanced efficiency in models like YOLOv5, EfficientNet, and MobileNet.2. Implemented the integration of optimization techniques with an automated graph network that allows seamless parameter grouping and facilitate Structural pruning to remove parameters physically. Proved instrumental in enhancing model efficiency by systematically eliminating redundant network components without compromising accuracy.
Masters Thesis
1. Engineered Video Analytics system on a Disaggregated Architecture of a cluster of heterogeneous NVIDIA Jetson devices.2. Implemented fast & memory efficient pipelines to optimize the performance of Deep Learning models on ML acceleration hardware by addressing performance bottlenecks.3. Orchestrated deployment on Kubernetes and streamlined machine learning workflows with Kubeflow integration.4. Utilizing sparsity, quantization-aware training (QAT) & post-training quantization (PTQ), operator fusion, pruning, ONNXRuntime & TensorRT optimizations to minimize latency & enhance throughput.5. Developed custom C++ operators (LayerNorm, GeLU) for TFLite, enabling real-time model inference on NPU.6. Deployed License Plate Recognition CI/CD pipeline using PyTorch & Docker with 1.93x increased processing rate.7. Developed a Split Computing algorithm (model sharding & model merging) to ensure optimized memory and computing resource allocation for deployment (CPU & GPU), efficiency and scalability for Deep Neural Networks.8. Published a thesis contributing to advancements in Machine Learning Infrastructure and Optimizations.
Research Collaborator
1. Developed a Qt (C++) application for recognizing Gujarati Script using EfficientNet B3 model for optical and hand-written text with an accuracy of 99.7%.2. Recognized text was further processed by using a Bi-LSTM model to rectify grammatical errors. Increased the recognition accuracy of conjunct consonants by 12% in comparison with traditional OCR models.3. The results of this project is accepted for publication in Procedia Computer Science, 2022.
Colleagues at AMD
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Rahul Malla
Colleague at AmdCuttack, Odisha, India
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JP
Jaylon Powers
Colleague at AmdColumbia, Tennessee, United States
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KW
Kristol Wayman
Colleague at AmdAustin, Texas Metropolitan Area, United States
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NP
Natraj P
Colleague at AmdBengaluru, Karnataka, India
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PD
Pete Dodd
Colleague at AmdClayton, Georgia, United States
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NW
Niyat Wali
Colleague at AmdKarachi Division, Sindh, Pakistan
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AA
Asad Ali
Colleague at AmdSukkur District, Sindh, Pakistan
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HQ
Hung Quang
Colleague at AmdQuận 4, Ho Chi Minh City, Vietnam, Viet Nam
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VS
Vipula Sharma
Colleague at AmdHyderabad, Telangana, India
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DC
Daniel Cosmos
Colleague at AmdAbuja, Federal Capital Territory, Nigeria
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Abhinav Sharma education
Master'S Degree, Computer Science
Bachelor Of Engineering - Be, Computer Engineering
Junior College, Science
Frequently asked questions about Abhinav Sharma
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What company does Abhinav Sharma work for?
Abhinav Sharma works for AMD.
What is Abhinav Sharma's role at AMD?
Abhinav Sharma is listed as Efficient ML @ AMD | Ex - Nokia Bell Labs | MS CS @ Stony Brook University at AMD.
Where is Abhinav Sharma based?
Abhinav Sharma is based in San Jose, California, United States while working with AMD.
What companies has Abhinav Sharma worked for?
Abhinav Sharma has worked for Amd, Nokia Bell Labs, Stony Brook University College Of Engineering And Applied Sciences, and Tata Institute Of Fundamental Research, Mumbai.
Who are Abhinav Sharma's colleagues at AMD?
Abhinav Sharma's colleagues at AMD include Rahul Malla, Jaylon Powers, Kristol Wayman, Natraj P, and Pete Dodd.
How can I contact Abhinav Sharma?
You can use AeroLeads to view verified contact signals for Abhinav Sharma at AMD, including work email, phone, and LinkedIn data when available.
What schools did Abhinav Sharma attend?
Abhinav Sharma holds Master'S Degree, Computer Science from Stony Brook University.
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