In this work, we propose Puppeteer, which is a hardware prefetcher manager that uses a suite of random forest regressors to determine at runtime which prefetcher should be ON at each level in the memory hierarchy, such that the prefetchers complement each other and we reduce the data/instruction access latency.
Recently, Intel introduced a new hardware feature for intra-process memory isolation, called Memory Protection Keys (MPK), which enables a user-space process to switch the domains in an efficient way.
Cryptography and Security Hardware Architecture
To close the gap between memory and processors, and in turn improve performance, there has been an abundance of work in the area of data/instruction prefetcher designs.
In this paper, we leveraged the power of Nvidia GPUs to parallelize all of the computation involved in training, to accelerate a backpropagation feed-forward neural network with one hidden layer using CUDA and C++.
When the features were reduced to a list of 20 genes, the ensemble algorithms maintained an accuracy above 95% as opposed to the clustering and classification models.
LEAF-QA being constructed from real-world sources, requires a novel architecture to enable question answering.
In this work, we present a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to CNNs and SVMs under tight energy budgets.