In this work, we explore the reuse of the components in a Vector Processing Unit (VPU) to offer the functionality of a Systolic Array (SA) for General Matrix Multiplication (GEMM), a kernel extensively used in machine learning, big data, and scientific computing.

2024年9月23日 · 本田HONDA车辆稳定系统VSA（Vehicle Stability Assist）是一种高级的主动安全系统，它基于ABS（Anti-Lock Braking System）系统，并扩展了其功能，以提高车辆在高速过弯或其他复杂驾驶条件下的稳定性。

2021年12月15日 · VSAs are a class of approaches to solve computational problems using mathematical operations on large vectors. A VSA consists of a particular vector space, for example \\([-1,1]^D\\) with \\(D=10,000\\) (the space of 10,000-dimensional vectors with real numbers between \\(-1\\) and 1) and a

vsapy - Vector Symbolic Architecture (VSA) library. This library implements the common methods used in hyperdimensional computing/Vector Symbolic Architectures. Namely bind, unbind and some bundling operations, bag, ShiftedBag, NamedBag and a …

2021年6月9日 · We demonstrate in this article that the field-like algebraic structure of VSA offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing.

2019年5月2日 · Vector Symbolic Architectures (VSAs) are ways of representing complex concepts in distributed representations using associative memory techniques. These representations can be used models for cognitive science and artificial intelligence. This package contains data structures and functions for implementing VSA schemes.

To solve this problem, we proposed a distributed service discovery method, named VSA-SD, based on the Vector Symbolic Architecture (VSA). This method employs hyperdimensional vectors to describe services in a distributed manner, and measures the degree of service matching by calculating the Hamming distance, thereby achieving service discovery.

2020年1月31日 · This paper provides an overview of eleven available VSA implementations and discusses their commonalities and differences in the underlying vector space and operators. We create a taxonomy of available binding operations and show an important ramification for non self-inverse binding operations using an example from analogical reasoning.

2024年5月14日 · In VSA-Flow, accurate optical flow estimation validates the effectiveness of HD feature descriptors. In VSA-SM, a novel similarity maximization method based on the HD feature descriptor is proposed to learn optical flow in a self-supervised way from events alone, eliminating the need for auxiliary grayscale images.

Vector Symbolic Architecture (s) (VSA) is a term coined by psychologist R. W. Gayler [1] to refer to a family of connectionist network models developed since the late 1980s. An alternative term Hyperdimensional Computing (HD) was proposed by neuroscientist P. Kanerva [7]. Nowadays, it is common to refer to the family as HD/VSA.