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Found 8 papers, 0 papers with code
Hydro: Adaptive Query Processing of ML Queries
Second, the optimal query plan for ML queries is data-dependent, necessitating DBMSs to adapt the query plan on the fly during execution.
VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs
Therefore, as DL workloads embrace sparsity to reduce the computations and memory size of models, it is also imperative for CPUs to add support for sparsity to avoid under-utilization of the dense matrix engine and inefficient usage of the caches and registers.
RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU
As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency.
Reducing Inference Latency with Concurrent Architectures for Image Recognition
Satisfying the high computation demand of modern deep learning architectures is challenging for achieving low inference latency.
LCP: A Low-Communication Parallelization Method for Fast Neural Network Inference in Image Recognition
To benefit from available compute resources with low communication overhead, we propose the first DNN parallelization method for reducing the communication overhead in a distributed system.
A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL
The sequence-to-sequence (seq2seq) model for neural machine translation has significantly improved the accuracy of language translation.
Collaborative Execution of Deep Neural Networks on Internet of Things Devices
In this paper, we propose an approach that utilizes aggregated existing computing power of Internet of Things (IoT) devices surrounding an environment by creating a collaborative network.
Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices
Furthermore, in image recognition, Musical Chair achieves similar performance and saves dynamic energy.
