Computing with memory: Difference between revisions

Revision as of 09:09, 24 July 2018

Revision as of 19:48, 10 March 2021

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{{technical|date=August 2012}}

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{{short description|Type of computing platform}}

'''Computing with Memory''' refers to computing platforms where function response is stored in memory array, either one or two-dimensional, in the form of [[lookup table]]s (LUTs) and functions are evaluated by retrieving the values from the LUTs. These computing platforms can follow either a purely spatial computing model, as in [[field-programmable gate array]] (FPGA), or a temporal computing model, where a function is evaluated across multiple clock cycles. The latter approach aims at reducing the overhead of programmable interconnect in FPGA by folding interconnect resources inside a computing element. It uses dense two-dimensional memory arrays to store large multiple-input multiple-output LUTs. Computing with Memory differs from Computing in Memory or [[processor-in-memory]] (PIM) concepts, widely investigated in the context of integrating a processor and memory on the same chip to reduce memory latency and increase bandwidth. These architectures seek to reduce the distance the data travels between the processor and the memory. The [[Berkeley IRAM project]] is one notable contribution in the area of PIM architectures.