64 citations · 316 across the 28 of their papers we have counts for
6 papers · 1 filter
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis
Abdullah M. Zyarah, Dhireesha Kudithipudi
Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatiotemporal inputs. This paper prese…
Semi-Trained Memristive Crossbar Computing Engine with In-Situ Learning Accelerator
Abdullah M. Zyarah, Dhireesha Kudithipudi
On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption. In this research, an on-device training circuitry for…
Neuromorphic Architecture for the Hierarchical Temporal Memory
Abdullah M. Zyarah, Dhireesha Kudithipudi
A biomimetic machine intelligence algorithm, that holds promise in creating invariant representations of spatiotemporal input streams is the hierarchical temporal memory (HTM). Thi…
Mod-DeepESN: Modular Deep Echo State Network
Zachariah Carmichael, Humza Syed, Stuart Burtner +1
Neuro-inspired recurrent neural network algorithms, such as echo state networks, are computationally lightweight and thereby map well onto untethered devices. The baseline echo sta…
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit
Seyed H. F. Langroudi, Tej Pandit, Dhireesha Kudithipudi
Performing the inference step of deep learning in resource constrained environments, such as embedded devices, is challenging. Success requires optimization at both software and ha…
On the Statistical Challenges of Echo State Networks and Some Potential Remedies
Qiuyi Wu, Ernest Fokoue, Dhireesha Kudithipudi
Echo state networks are powerful recurrent neural networks. However, they are often unstable and shaky, making the process of finding an good ESN for a specific dataset quite hard.…