%0 Conference Paper %B International Symposium on Computer Architecture (ISCA) %D 2016 %T Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators %A Brandon Reagen %A Paul Whatmough %A Robert Adolf %A Saketh Rama %A Hyunkwang Lee %A Sae Kyu Lee %A José Hernández-Lobato %A Gu Wei %A David Brooks %K accelerators %K dnn %K machine learning %K power %X The continued success of Deep Neural Networks (DNNs) in classification tasks has sparked a trend of accelerating their execution with specialized hardware. While published designs easily give an order of magnitude improvement over general-purpose hardware, few look beyond an initial implementation. This paper presents Minerva, a highly automated co-design approach across the algorithm, architecture, and circuit levels to optimize DNN hardware accelerators. Compared to an established fixed-point accelerator baseline, we show that fine-grained, heterogeneous datatype optimization reduces power by 1.5×; aggressive, inline predication and pruning of small activity values further reduces power by 2.0×; and active hardware fault detection coupled with domain-aware error mitigation eliminates an additional 2.7× through lowering SRAM voltages. Across five datasets, these optimizations provide a collective average of 8.1× power reduction over an accelerator baseline without compromising DNN model accuracy. Minerva enables highly accurate, ultra-low power DNN accelerators (in the range of tens of milliwatts), making it feasible to deploy DNNs in power-constrained IoT and mobile devices. %B International Symposium on Computer Architecture (ISCA) %C Seoul, Korea (South) %G eng %U https://doi.org/10.1109/ISCA.2016.32