Several manufacturers have already started to commercialize near-bank Processing-In- Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …
Quantization is a technique to reduce the computation and memory cost of DNN models, which are getting increasingly large. Existing quantization solutions use fixed-point integer …
As its core computation, a self-attention mechanism gauges pairwise correlations across the entire input sequence. Despite favorable performance, calculating pairwise correlations is …
Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference. A promising category of accelerators utilizes …
S Alam, C Yakopcic, Q Wu, M Barnell, S Khan… - Electronics, 2024 - mdpi.com
The unprecedented progress in artificial intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for …
Nanopore sequencing is a widely-used high-throughput genome sequencing technology that can sequence long fragments of a genome into raw electrical signals at low cost …
With the ever increasing prevalence of neural networks and the upheaval from the language models, it is time to rethink neural acceleration. Up to this point, the broader research …
B Kim, S Li, H Li - 2023 IEEE International Symposium on High …, 2023 - ieeexplore.ieee.org
This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA), supporting inference and training for deep neural networks (DNNs). Processing-in-memory …
F Liu, W Zhao, Z Wang, Y Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Resistive Random-Access-Memory (ReRAM) crossbar is one of the most promising neural network accelerators, thanks to its in-memory and in-situ analog computing abilities for …