# 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

1 code implementation1 Jul 2021,

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.

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# 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 $q$.

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# Similarity Search with Tensor Core Units

no code implementations22 Jun 2020,

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 $q$.

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# 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.

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