SPINDLE: SPINtronic Deep Learning Engine for large-scale neuromorphic computing

By Shankar Ganesh Ramasubramanian, Rangharajan Venkatesan, Mrigank Sharad, Kaushik Roy, Anand Raghunathan. 2014

86 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. M. Mohammadi
2. Ala Al-Fuqaha
3. Sameh Sorour
4. Mohsen Guizani
5. Catherine D. Schuman
6. Swagath Venkataramani
7. Anand Raghunathan
8. Thomas E. Potok

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Accelerators [91] [185] [190] [191] [192] [193] • Integrates DL model with the hardware • Efficient computations • Does not work with the traditional hardware platforms”
• A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, background): “[2130], [2585], [2591], [2615], car recognition or detection”
• Efficient embedded learning for IoT devices (2016, methodology): “Such architectures achieve over 15× improvement in energy compared to well-optimized CMOS designs [32].”
• Conditional Deep Learning for energy-efficient and enhanced pattern recognition (2015, methodology): “For instance, SuperVision [9], a DLN that won the ImageNet visual recognition challenge, demands compute performance in the order of 2-4 GOPS per classification [10].”