Show simple item record

dc.contributor.authorWaterland, Amosen_US
dc.contributor.authorAppavoo, Jonathanen_US
dc.contributor.authorSchatzberg, Danen_US
dc.date.accessioned2015-06-24T19:48:46Z
dc.date.available2015-06-24T19:48:46Z
dc.date.issued2012-04-15en_US
dc.identifier.citationWaterland, Amos; Appavoo, Jonathan; Schatzberg, Dan. "Programmable Smart Machines", Technical Report BUCS-TR-2012-007, Computer Science Department, Boston University, April 15, 2012. [Available from: http://hdl.handle.net/2144/11395]en_US
dc.identifier.urihttps://hdl.handle.net/2144/11395
dc.description.abstractIn this paper we conjecture that a system can be constructed that exploits the general ability to learn through the counting, correlating, and memorizing of occurrences of events to fast-forward a programmable computer. In particular, we propose a signal based interpretation of a computer's execution that can be used to implement a form of system state memoization using a predictive associative memory. Such an approach may some day lead to a system that can utilize both traditional logic and neuromorphic or other biologically inspired mechanisms to be both programmable and smart.en_US
dc.description.sponsorshipDepartment of Energy Office of Science (DE-SC0005365), National Science Foundation (1012798)en_US
dc.language.isoen_USen_US
dc.publisherComputer Science Department, Boston Universityen_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-2012-007en_US
dc.titleProgrammable smart machinesen_US
dc.typeTechnical Reporten_US


Files in this item

This item appears in the following Collection(s)

Show simple item record