In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
A: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Rationale: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.
Q: Machine learning workloads are often expensive to train, taking weeks to converge. The current generation of frameworks relies on custom back-ends in order to achieve efficiency, making it impractical to train models on less common hardware where no such back-ends exist. Knossos builds on recent work that avoids the need for hand-written libraries, instead compiles machine learning models in much the same way one would compile other kinds of software. In order to make the resulting code efficient, the Knossos complier directly optimises the abstract syntax tree of the program. However in contrast to traditional compilers that employ hand-written optimisation passes, we take a rewriting approach driven by the $A^\star$ search algorithm and a learn value function that evaluates future potential cost reduction of taking various rewriting actions to the program. We show that Knossos can automatically learned optimisations that past compliers had to implement by hand. Furthermore, we demonstrate that Knossos can achieve wall time reduction compared to a hand-tuned compiler on a suite of machine learning programs, including basic linear algebra and convolutional networks. The Knossos compiler has minimal dependencies and can be used on any architecture that supports a \Cpp toolchain.
Since cost model the proposed algorithm optimises can be tailored to a particular hardware architecture, the proposed approach can potentially applied to a variety of hardware.
A:
We combine A* search with reinforcement learning to speed up machine learning code