Compute-in-ROM : a in-memory activation-aware dot-product compute macro for low-cost edge devices

dc.contributor.advisorKulkarni, Jaydeep P.
dc.creatorNi, Can
dc.date.accessioned2024-06-04T01:55:33Z
dc.date.available2024-06-04T01:55:33Z
dc.date.issued2021-05
dc.date.submittedMay 2021
dc.date.updated2024-06-04T01:55:33Z
dc.description.abstractCompute-in-Memory (CiM) approaches perform multiply-and-accumulate (MAC), or dot-product, arithmetic operations required for neural network (NN) within the memory array that stores weight. Many CiM works within different memory technologies have reported superior energy efficiency compared to conventional digital accelerators. To overcome reliability and cost issues that are present in other memory arrays, this report details a CiM approach within in Read-Only Memory (ROM) that features (1) multi-level cell (MLC) for dense storage, (2) accumulation performed through current summation that has improved robustness to process variation, (3) capacitor- and resistor-less compact peripheral to enable analog compute, and (4) ReLU activation-aware MAC compute for early termination of computing negative pre-activation to achieve better energy efficiency.
dc.description.departmentElectrical and Computer Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/2152/125646
dc.identifier.urihttps://doi.org/10.26153/tsw/52207
dc.language.isoen
dc.subjectMachine learning hardware
dc.subjectCompute-in-memory
dc.titleCompute-in-ROM : a in-memory activation-aware dot-product compute macro for low-cost edge devices
dc.typeThesis
dc.type.materialtext
local.embargo.lift2022-05-01
local.embargo.terms2022-05-01
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.nameMaster of Science in Engineering

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