This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …

Abstract Processing-in-memory (PIM) techniques have gained much attention from computer
architecture researchers, and significant research effort has been invested in exploring and …

Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies
on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple …

In the Internet of Things (IoT) domain, many applications are running machine learning
algorithms to assimilate the data collected in the swarm of devices. Sending all data to the …

Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the
brain's structure to offer a powerful and efficient processing and learning model. In HDC, the …

Nearest neighbor (NN) search computations are at the core of many applications such as
few-shot learning, classification, and hyperdimensional computing. As such, efficient …

Advances in graph neural network (GNN)-based algorithms enable machine learning on
relational data. GNNs are computationally demanding since they rely upon backpropagation …

The biosignals consist of several sensors that collect time series information. Since time
series contain temporal dependencies, they are difficult to process by existing machine …

The separation of computing units and memory in the computer architecture mandates
energy-intensive data transfers creating the von Neumann bottleneck. This bottleneck is …

Brain-inspired Hyperdimensional Computing (HDC) is an emerging framework in low-
energy designs for solving classification tasks at the edge. Unlike mainstream neural …