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Found 8 papers, 3 papers with code
A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures
This survey provides a holistic review of the most influential design methodologies and EDA tools proposed in recent years to implement Deep Learning accelerators, offering the reader a wide perspective in this rapidly evolving field.
On the Bike Spreading Problem
A free-floating bike-sharing system (FFBSS) is a dockless rental system where an individual can borrow a bike and returns it anywhere, within the service area.
Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?
Given a set of points $S$ and a radius parameter $r>0$, the $r$-near neighbor ($r$-NN) problem asks for a data structure that, given any query point $q$, returns a point $p$ within distance at most $r$ from $q$.
Similarity Search with Tensor Core Units
Tensor Core Units (TCUs) are hardware accelerators developed for deep neural networks, which efficiently support the multiplication of two dense $\sqrt{m}\times \sqrt{m}$ matrices, where $m$ is a given hardware parameter.
A Computational Model for Tensor Core Units
To respond to the need of efficient training and inference of deep neural networks, a plethora of domain-specific hardware architectures have been introduced, such as Google Tensor Processing Units and NVIDIA Tensor Cores.
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions
There are several variants of the similarity search problem, and one of the most relevant is the $r$-near neighbor ($r$-NN) problem: given a radius $r>0$ and a set of points $S$, construct a data structure that, for any given query point $q$, returns a point $p$ within distance at most $r$ from $q$.
Symmetry-free SDP Relaxations for Affine Subspace Clustering
We consider clustering problems where the goal is to determine an optimal partition of a given point set in Euclidean space in terms of a collection of affine subspaces.
On the Complexity of Inner Product Similarity Join
* New upper and lower bounds for (A)LSH-based algorithms.
