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Found 6 papers, 1 papers with code
DaCapo: Accelerating Continuous Learning in Autonomous Systems for Video Analytics
Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-driving vehicles, unmanned aerial vehicles (UAVs), and security robots.
Accelerating String-Key Learned Index Structures via Memoization-based Incremental Training
Learned indexes use machine learning models to learn the mappings between keys and their corresponding positions in key-value indexes.
CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics
This paper presents CoVA, a novel cascade architecture that splits the cascade computation between compressed domain and pixel domain to address the decoding bottleneck, supporting both temporal and spatial queries.
FlexBlock: A Flexible DNN Training Accelerator with Multi-Mode Block Floating Point Support
While several prior works proposed such multi-precision support for DNN accelerators, not only do they focus only on the inference, but also their core utilization is suboptimal at a fixed precision and specific layer types when the training is considered.
Multi-model Machine Learning Inference Serving with GPU Spatial Partitioning
To maximize the resource efficiency of inference servers, a key mechanism proposed in this paper is to exploit hardware support for spatial partitioning of GPU resources.
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks
Compared to Stripes, BitFusion provides 2. 6x speedup and 3. 9x energy reduction at 45 nm node when BitFusion area and frequency are set to those of Stripes.
