Efficient and robust associative memory from a generalized Bloom filter.
We develop a variant of a Bloom filter that is robust to hardware failure and show how it can be used as an efficient associative memory. We define a measure of the information recall and show that our new associative memory is able to recall more than twice as much information as a Hopfield network. The extra efficiency of our associative memory is all the more remarkable as it uses only bits while the Hopfield network uses integers.
Cavendish Laboratory, Cambridge University, Cambridge, UK. sterne@fias.uni-frankfurt.de
P
P
Sterne
D000465:Algorithms; D016248:Computer Storage Devices; D003199:Computer Systems; D003491:Cybernetics; D015233:Models, Statistical; D016571:Neural Networks, Computer