… from deep learning [1] – an innovative area of machine learning that is … The range of inference
tasks impacted by deep learning … However surprisingly, even though such inferences are …

… computations during inference with Deep Learning models. … needed by multiplications during
inference can be potentially … energy efficiency for inference with Deep Learning Networks. …

… This paper introduces the Deep Learning Inference Service, an online production service at
… -latency deep neural network model inference. We present the system architecture and deep …

… Here, we present a deep learning method for gene expression inference (D-GEX). D-GEX is
… different strategies and tried to interpret the advantages of deep learning compared with LR. …

… We study the accuracy and behavior of deep learning networks when applied to type
inference across a range of metrics (see Section 4.4). Our proposed model enhances a …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data-…

… to high-performance DNN inference algorithms has not been … inference accelerators as
semiconductor scaling slows. This paper presents Simba, a scalable deep-learning inference ac…

… The application of deep learning techniques resulted in remarkable improvement of
machine learning models. In this paper we provide detailed characterizations of deep learning …

… In the paper, we presented a cost-efficient deep learning inference system named Scrooge,
which packs DL workload efficiently to maximize inference throughput while satisfying DL …

… the computational performance of deep learning applications. It is … Once trained, neural
networks can be deployed for inference … quantization for neural network inference, where trained …