Network-based hyperdimensional system

1. A network-based hyperdimensional system comprising:

an encoder implemented in hardware and configured to:

receive input data in a bitstream;

divide the bit stream into chunks;

generate chunk hypervectors from the chunks;

generate a position hypervector for each chunk hypervector;

bind each position hypervector with each associated chunk hypervector to generate bound hypervectors; and

add the bound hypervectors together to generate encoded data bits; and

a decoder implemented in hardware and configured to:

receive the encoded data bits over a network; and

decode the encoded data bits by comparing predetermined bit sequences with the encoded data bits until a convergence to a maximum match between the predetermined bit sequences and the encoded data bits occurs within a finite number of decoding iterations and thereby reconstruct the input data from the decoded data bits.

2. The network-based hyperdimensional system of claim 1 wherein the finite number of decoding iterations is between 1 and 10.

3. The network-based hyperdimensional system of claim 1 wherein no error correction is performed by the decoder.

4. The network-based hyperdimensional system of claim 1 wherein the decoder further comprises a hardware accelerator with content addressable memory (CAM) that stores the predetermined bit sequences in a look-up table.

5. The network-based hyperdimensional system of claim 4 wherein the CAM is configured for row-parallel search operations.

6. The network-based hyperdimensional system of claim 4 wherein the CAM is configured for searching hypervectors that have real and imaginary components.

7. The network-based hyperdimensional system of claim 6 wherein both the real and imaginary components are nonzero.

8. The network-based hyperdimensional system of claim 6 wherein the real and imaginary components are stored in adjacent memory cells of the CAM.

9. The network-based hyperdimensional system of claim 8 wherein the CAM is further configured to determine Hamming distance of both the real and imaginary components.

10. The network-based hyperdimensional system of claim 9 wherein the CAM is configured to determine the Hamming distances by sensing discharging current on match-lines that are CAM rows.

11. The network-based hyperdimensional system of claim 1 wherein the encoded data represent a totality of the bit stream.

12. The network-based hyperdimensional system of claim 1 wherein the encoder is configured to operate based on parameters that include but are not limited to chunk size C representing a total number of bits to be transmitted per layer, dimension D representing a number of channels to be transmitted, and a number of layers V to be encoded.

13. The network-based hyperdimensional system of claim 12 wherein the chunk size C multiplied by the number of layers V to be encoded is equal to the total number of bits to be transmitted.

14. The network-based hyperdimensional system of claim 13 wherein a coding rate of the encoder is equal to the total number of bits to be transmitted C×V divided by the dimension D.

15. The network-based hyperdimensional system of claim 1 wherein the encoder and decoder hardware implementations include but are not limited to field programmable gate arrays, graphics processing units, central processing units and CAM.

16. The network-based hyperdimensional system of claim 1 wherein the HDC is configured to perform single-pass training.

17. The network-based hyperdimensional system of claim 1 further comprising a learning module that is configured to perform classification and clustering of the input data.

18. The network-based hyperdimensional system of claim 1 wherein the encoder is configured to provide dynamic data compression during encoding of the input data.