Cognitive Computing Safety: The New Horizon for Reliability / The Design and Evolution of Deep Learning Workloads

This column includes two invited position papers about the challenges and opportunities in cognitive architectures.

[1]  James Tschanz,et al.  Parameter variations and impact on circuits and microarchitecture , 2003, Proceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451).

[2]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[3]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[4]  Song Han,et al.  EIE: Efficient Inference Engine on Compressed Deep Neural Network , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

[5]  Luiz André Barroso,et al.  The tail at scale , 2013, CACM.

[6]  Olivier Temam,et al.  A defect-tolerant accelerator for emerging high-performance applications , 2012, 2012 39th Annual International Symposium on Computer Architecture (ISCA).

[7]  Amin Ansari,et al.  Using Multiple Input, Multiple Output Formal Control to Maximize Resource Efficiency in Architectures , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

[8]  David I. August,et al.  Automatic Instruction-Level Software-Only Recovery , 2006, IEEE Micro.

[9]  Gu-Yeon Wei,et al.  Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

[10]  Gu-Yeon Wei,et al.  Fathom: reference workloads for modern deep learning methods , 2016, 2016 IEEE International Symposium on Workload Characterization (IISWC).

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Flaviu Cristian,et al.  Understanding fault-tolerant distributed systems , 1991, CACM.