Key job responsibilities * Research and implement the state-of-the-art computer vision and sensor fusion algorithms for resource-constrained computing platforms on a large scale. You will collaborate with different Amazon teams to make informed decisions on the best practices in machine learning to build highly-optimized integrated hardware and software platforms. This position requires experience with developing efficient computer vision algorithms on resource-constrained computing platforms on edge. This is a great opportunity for you to innovate in this space by developing highly optimized algorithms that will work on scale. You will be part of a team committed to pushing the frontier of computer vision and machine learning technology to deliver the best experience for our neighbors. Our evaluation demonstrates that Nimble outperforms existing solutions for dynamic neural networks by up to 20× on hardware platforms including Intel CPUs, ARM CPUs, and Nvidia GPUs.Īre you a passionate scientist in the computer vision area who is aspired to apply your skills to bring value to millions of customers? Here at Ring, we have a unique possibility to innovate and see how the results of our work improve the lives of millions of people and make neighborhoods safer. Nimble handles model dynamism by introducing a dynamic type system, a set of dynamism-oriented optimizations, and a light-weight virtual machine runtime. This paper proposes Nimble, a high-performance and flexible system to optimize, compile, and execute dynamic neural networks on multiple platforms. Optimizing dynamic neural networks is more challenging than static neural networks optimizations must consider all possible execution paths and tensor shapes. Therefore, executing dynamic models with deep learning systems is currently both inflexible and sub-optimal, if not impossible. Existing deep learning systems focus on optimizing and executing static neural networks which assume a pre-determined model architecture and input data shapes-assumptions that are violated by dynamic neural networks. Modern deep neural networks increasingly make use of features such as control flow, dynamic data structures, and dynamic tensor shapes.
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