Name
Title | ||
|---|---|---|
Hardware architecture of the Protein Processing Associative Memory and the effects of dimensionality and quantisation on performance |
Abstract | ||
|---|---|---|
The Protein Processor Associative Memory (PPAM) is a novel hardware architecture for a distributed, decentralised, robust and scalable, bidirectional, hetero-associative memory, that can adapt online to changes in the training data. The PPAM uses the location of data in memory to identify relationships and is therefore fundamentally different from traditional processing methods that tend to use arithmetic operations to perform computation. This paper presents the hardware architecture and details a sample digital logic implementation with an analysis of the implications of using existing techniques for such hardware architectures. It also presents the results of implementing the PPAM for a robotic application that involves learning the forward and inverse kinematics. The results show that, contrary to most other techniques, the PPAM benefits from higher dimensionality of data, and that quantisation intervals are crucial to the performance of the PPAM. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10710-014-9217-1 | Genetic Programming and Evolvable Machines |
Keywords | Field | DocType |
Protein processing,PPAM,FPGA,Associative memory,BERT2,Inverse kinematics,Dimensionality,Quantisation,Non-standard computation | Content-addressable memory,Inverse kinematics,Computer science,Field-programmable gate array,Curse of dimensionality,Boolean algebra,Artificial intelligence,Machine learning,Scalability,Hardware architecture,Computation | Journal |
Volume | Issue | ISSN |
15 | 3 | 1389-2576 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omer Qadir | 1 | 11 | 1.60 |
| Alexander Lenz | 2 | 0 | 1.01 |
| Gianluca Tempesti | 3 | 457 | 57.09 |
| Jon Timmis | 4 | 1237 | 120.32 |
| Tony Pipe | 5 | 171 | 24.02 |
| Andy M. Tyrrell | 6 | 629 | 73.61 |