The evicted-address filter: A unified mechanism to address both cache pollution and thrashing

By Vivek Seshadri, Onur Mutlu, Michael A. Kozuch, Todd C. Mowry. International Conference on Parallel Architectures and Compilation Techniques, 2012

407 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/65cd7e21193ced281fecb0894a1245a8c3988286

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Citations in the corpus

Food-101 - Mining Discriminative Components with Random Forests (2014, background): “[39] propose to learn spatial relationships between ingredients using pairwise features.”
A Survey on Food Computing (2018, methodology): “Relevant works include both hand-crafted [Hoashi et al. 2010; Martinel et al. 2015; Yang et al. 2010] and deep [Kagaya et al. 2014; Martinel et al. 2018; Wu et al. 2016] representations for recognition.”
Memory scaling: A systems architecture perspective (2013, background): “, pages, cache blocks) within the memory system leads to large inefficiency when most of the large granularity is not needed [76, 77, 50, 49, 72, 67, 37, 63, 30].”
Food Detection and Recognition Using Convolutional Neural Network (2014, methodology): “The Pittsburgh Fast-Food Image Dataset [4] is a dataset of American fast-food images, which was used to evaluate a food-recognition method in [11, 6].”
Fine-Grained Image Classification by Exploring Bipartite-Graph Labels (2015, background): “Fine-grained image classification concerns the task of distinguishing sub-ordinate categories of some base classes such as dogs [26, 43], birds [5, 8], flowers [1, 40], plants [32, 45], cars [30, 36, 48], food [3, 6, 38, 59], clothes [14], fonts [10] and furniture [4].”
Food image recognition using deep convolutional network with pre-training and fine-tuning (2015, background): “Therefore, many works have been published so far [1, 2, 3, 4, 5, 6, 7].”
A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure (2018, methodology): “In another study [7], the authors proposed a method for fast food detection by”
Recognition of Multiple-Food Images by Detecting Candidate Regions (2012, methodology): “They achieved the better classification rate than S. Yang et al.’s results on the same fast food dataset.”
Deep-based Ingredient Recognition for Cooking Recipe Retrieval (2016, background): “One early model is PFD (pairwise local feature distribution) [37], which leverages the result of ingredient recognition for food categorization.”
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern (2014, background): “Color information is crucial for many applications, such as flower recognition [30], [31], [32], food recognition [17].”
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment (2016, methodology): “Yang et al. [6] proposed a method to recognize fast food using the relative spatial relationships of local features of the ingredients followed by a feature fusion method.”
MASK: Redesigning the GPU Memory Hierarchy to Support Multi-Application Concurrency (2018, background): “Cache insertion policies that account for cache thrashing [55, 56, 108], future reuse [116, 117], or inter-application interference [144] work well for CPU applications, but previous works have shown that some of these policies can be ineffective for GPU applications [16, 74].”
Tiered-latency DRAM: A low latency and low cost DRAM architecture (2013, background): “, [8, 17, 18, 36, 37, 38, 46]) have proposed sophisticated cache management policies in the context of processor SRAM caches.”