Yodann: An ultra-low power convolutional neu- ral network accelerator based on binary weights. In VLSI (ISVLSI), 2016 IEEE Computer Society Annual Symposium on, pages 236–241. IEEE, 2016. [18] Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, and Yi Ma. Single-image crowd counting...
cost-performance comparison of various accelerator implementation platforms for deep convolutional neural networkDue to the high accuracy, DCNN is a popular deep learning approach for object recognition and classification. But, the computing complexity of DCNN is too high for real-time application. ...
Analogue in-memory computing (AIMC) with resistive memory devices could reduce the latency and energy consumption of deep neural network inference tasks by directly performing computations within memory. However, to achieve end-to-end improvements in latency and energy consumption, AIMC must be combined...
Fuzzy data comparator with neural network postprocessor A fuzzy data comparator receives a fuzzy data digital data bit stream and compares each frame thereof with multiple sets of differing known data stored in a plurality of pattern memories, using a selected comparison metric. The results o......
UNPU: an energy-efficient deep neural network accelerator with fully variable weight bit precision. IEEE J. Solid-State Circuits 54, 173–185 (2019). Article ADS Google Scholar Hill, P. et al. DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and ear-...
The comparison study in Background In this section, we first present the characteristics of the hardware accelerators evaluated in this study. Then, we briefly discuss three vision libraries and neural network inference frameworks that are widely used with these accelerators. We group the vision ...
been conducted, for example, in weather and climate modeling [40], for PINNs in fluid mechanics [41], in form of a comparison between DEM and graph neural networks (GNN) for solid mechanics [42] and between the two neural operator network methods DeepONet and FNO for general PDEs [43]....
This comparison also shows, however, that the analogue memory device of Fig. 5 actually performs worse across the board for LSTM, CNN, and Transformer models relative to the device described in Fig. 4, when both are evaluated in the limit of what is optimally achievable with either device. ...
Hanson S, Pratt L (1988) Comparing biases for minimal network construction with back-propagation. Adv Neural Inf Process Syst 1:177–185 Google Scholar Hassibi B, Stork DG, Wolff G, Watanabe T (1994) Optimal brain surgeon: extensions and performance comparison. In: Cowan JD, Tesauro G, Al...
One embodiment of an accelerator includes a computing unit; a first memory bank for storing input activations and a second memory bank for storing parameters used in performing comp