Hypervectors can be examined for similarities and differences using the fundamental Hyperdimensional Computing operations
Ultimately, a class hypervector that represents every class element is created by bundling the hypervectors within each class
After training creates class hypervectors from input feature vectors, each new feature vector is turned to one
Hyperdimensional Computing can be implemented in hardware with minimal power consumption because of its simplicity and the binary nature of its operations
Hyperdimensional Computing's distributed architecture allows parallel processing like the brain
Due to its great dimensionality, Hyperdimensional Computing is intrinsically resistant to mistakes and noise
With Intel oneAPI Base Toolkit, Altera FPGAs may immediately implement SYCL/C++ Hyperdimensional Computing applications
Hyperdimensional Computing Picture Categorization using Altera FPGAs