Presentation
UNICON: A Unified Reconfigurable Nonlinear Architecture for Efficient Neural Network Inference
DescriptionNonlinear activation functions (NAFs) are critical to deep neural networks (DNNs), yet their diverse and complex computational forms are inherently hardware-unfriendly, incurring substantial latency, area, and energy overheads. This work presents UNICON, a unified reconfigurable hardware architecture that efficiently supports diverse NAFs through a logarithmic-domain computing paradigm.
UNICON uncovers the intrinsic correlations among NAFs and decomposes complex nonlinear operations into lightweight shift–add operations. With modular design and dynamic reconfigurable dataflows, UNICON achieves high functional flexibility and resource efficiency without hardware duplication. As the first algorithm–architecture co-designed solution that unifies diverse NAFs within a logarithmic-domain framework, UNICON gains an average 1.55x speedup, 2.56x energy efficiency, and 2.74x area efficiency over state-of-the-art NAF architecture.
UNICON uncovers the intrinsic correlations among NAFs and decomposes complex nonlinear operations into lightweight shift–add operations. With modular design and dynamic reconfigurable dataflows, UNICON achieves high functional flexibility and resource efficiency without hardware duplication. As the first algorithm–architecture co-designed solution that unifies diverse NAFs within a logarithmic-domain framework, UNICON gains an average 1.55x speedup, 2.56x energy efficiency, and 2.74x area efficiency over state-of-the-art NAF architecture.
Event Type
Research Manuscript
TimeWednesday, July 2911:10am - 11:23am PDT
LocationMtg Room 101A
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