Robust and Scalable Hyperdimensional Computing With Brain-Like Neural
  Adaptations
Junyao Wang Mohammad Abdullah Al Faruque
The Internet of Things (IoT) has facilitated many applications utilizing
edge-based machine learning (ML) methods to analyze locally collected data.
Unfortunately, popular ML algorithms often require intensive computations
beyond the capabilities of today's IoT devices. Brain-inspired hyperdimensional
computing (HDC) has been introduced to address this issue. However, existing
HDCs use static encoders, requiring extremely high dimensionality and hundreds
of training iterations to achieve reasonable accuracy. This results in a huge
efficiency loss, severely impeding the application of HDCs in IoT systems. We
observed that a main cause is that the encoding module of existing HDCs lacks
the capability to utilize and adapt to information learned during training. In
contrast, neurons in human brains dynamically regenerate all the time and
provide more useful functionalities when learning new information. While the
goal of HDC is to exploit the high-dimensionality of randomly generated base
hypervectors to represent the information as a pattern of neural activity, it
remains challenging for existing HDCs to support a similar behavior as brain
neural regeneration. In this work, we present dynamic HDC learning frameworks
that identify and regenerate undesired dimensions to provide adequate accuracy
with significantly lowered dimensionalities, thereby accelerating both the
training and inference.