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Loom: exploiting weight and activation precisions to accelerate convolutional neural networks.

, , , , and . DAC, page 20:1-20:6. ACM, (2018)

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Difficulties of T1 brain MRI segmentation techniques., , , , and . Medical Imaging: Image Processing, volume 4684 of SPIE Proceedings, SPIE, (2002)Loom: exploiting weight and activation precisions to accelerate convolutional neural networks., , , , and . DAC, page 20:1-20:6. ACM, (2018)Laconic deep learning inference acceleration., , , , , , , and . ISCA, page 304-317. ACM, (2019)Memory Requirements for Convolutional Neural Network Hardware Accelerators., , , and . IISWC, page 111-121. IEEE Computer Society, (2018)Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning., , , , , , , , , and 3 other author(s). NEWCAS, page 356-360. IEEE, (2018)Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks., , , , , , , , and . ASPLOS, page 749-763. ACM, (2019)ShapeShifter: Enabling Fine-Grain Data Width Adaptation in Deep Learning., , , , , , , , , and 1 other author(s). MICRO, page 28-41. ACM, (2019)Diffy: a Déjà vu-Free Differential Deep Neural Network Accelerator., , and . MICRO, page 134-147. IEEE Computer Society, (2018)Essence-Based Clustering: A multi-strategic and highly-customizable clustering approach., , and . LA-CCI, page 1-6. IEEE, (2016)