Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
This work addresses a scalability bottleneck in HDC for researchers and practitioners dealing with high-dimensional data, representing an incremental improvement over existing encoding methods.
The paper tackles the scalability issue in hyperdimensional computing (HDC) when mapping high-dimensional input data by introducing streaming encoding techniques based on hashing, showing that these methods achieve comparable performance guarantees while being substantially more efficient, as validated on a high-dimensional classification problem with scalability to very large datasets.
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience. HDC represents data as high-dimensional, low-precision vectors which can be used for a variety of information processing tasks like learning or recall. The mapping to high-dimensional space is a fundamental problem in HDC, and existing methods encounter scalability issues when the input data itself is high-dimensional. In this work, we explore a family of streaming encoding techniques based on hashing. We show formally that these methods enjoy comparable guarantees on performance for learning applications while being substantially more efficient than existing alternatives. We validate these results experimentally on a popular high-dimensional classification problem and show that our approach easily scales to very large data sets.