Implementing Sparse Estimation: Cyclic Coordinate Descent vs Linearized Bregman Iterations
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Implementing sparse estimation ef?ciently in digital hardware is crucial for real-time applications. For such an implementation one typically favours lightweight iterative algorithms. This not only keeps the complexity low, but also allows a ?ne-granular tuning of the performance/complexity trade-off. Recently, algorithms based on Linearized Bregman Iterations (LBI) have shown to be very feasible for low complexity digital hardware implementation. An alternative approach would be to use cyclic coordinate descent (CCD) algorithms. However, the state-of-the-art formulation of sparse cyclic coordinate descent has properties preventing an ef?cient hardware implementation. In this work, we propose variations of cyclic coordinate descent, speci?cally tailored for digital ef?cient hardware implementation. These modi?cations allow cyclic coordinate descent algorithms to be competitive in a hardware implementation compared to the implementation ef?cient Linearized Bregman iteration algorithms. We show simulation results for different sparse estimation use-cases demonstrating the capabilities of both methods. We also identify scenarios where our CCD approach allows to obtain the same performance with less complexity than LBI.
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