University of California

Fengbo Ren

Dejan Markovic

Chih-Kong Ken Yang

Kang-Lung Wang

Systems and methods for implementing a scalable very-large-scale integration (VLSI) architecture to perform compressive sensing (CS) hardware reconstruction for data signals in accordance with embodiments of the invention are disclosed. The VLSI architecture is optimized for CS signal reconstruction by implementing a reformulation of the orthogonal matching pursuit (OMP) process and utilizing architecture resource sharing techniques. Typically, the VLSI architecture is a CS reconstruction engine that includes a vector and scalar computation cores where the cores can be time-multiplexed (via dynamic configuration) to perform each task associated with OMP. The vector core includes configurable processing elements (PEs) connected in parallel. Further, the cores can be linked by data-path memories, where complex data flow of OMP can be customized utilizing local memory controllers synchronized by a top-level finite-state machine. The computing resources (cores and data-paths) can be reused across the entire OMP process resulting in optimal utilization of the PEs.

Scalable and parameterized VLSI architecture for compressive sensing sparse approximation

As memory geometries continue to scale down, current density of magnetic tunnel junctions (MTJs) make conventional low current reading scheme problematic with regard to performance and reliability. A body-voltage sense circuit (BVSC) short pulse reading (SPR) circuit is described using body connected load transistors and a novel sensing circuit with second stage amplifier which allows for very short read pulses providing much higher read margins, less sensing time, and shorter sensing current pulses. Simulation results (using 65-nm CMOS model SPICE simulations) show that our technique can achieve 550 mV of read margin at 1 ns performance under a 1 V supply voltage, which is greater than reference designs achieve at 5 ns performance.

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Los Angeles, CA, United States of America

Fengbo Ren