New research paper titled “Neural sampling machine with stochastic synapse allows brain-like learning and inference” from University of Notre Dame and Department of Cognitive Sciences, University of California Irvine.
Abstract “Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Brain-inspired probabilistic models of neural network can explicitly handle the uncertainty in data and allow adaptive learning on the fly. However, their implementation in a compact, low-power hardware remains a challenge. In this work, we introduce a novel hardware fabric that can implement a new class of stochastic neural network called Neural Sampling Machine (NSM) by exploiting the stochasticity in the synaptic connections for approximate Bayesian inference. We experimentally demonstrate an in silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor (FeFET)-based analog weight cell with a two-terminal stochastic selector element. We show that the stochastic switching characteristic of the selector between the insulator and the metallic states resembles the multiplicative synaptic noise of the NSM. We perform network-level simulations to highlight the salient features offered by the stochastic NSM such as performing autonomous weight normalization for continual online learning and Bayesian inferencing. We show that the stochastic NSM can not only perform highly accurate image classification with 98.25% accuracy on standard MNIST dataset, but also estimate the uncertainty in prediction (measured in terms of the entropy of prediction) when the digits of the MNIST dataset are rotated. Building such a probabilistic hardware platform that can support neuroscience inspired models can enhance the learning and inference capability of the current artificial intelligence (AI).”
Find the open access technical paper here. Published May 2022.
Dutta, S., Detorakis, G., Khanna, A. et al. Neural sampling machine with stochastic synapse allows brain-like learning and inference. Nat Commun 13, 2571 (2022). https://doi.org/10.1038/s41467-022-30305-8
Visit Semiconductor Engineering’s Technical Paper library here and discover many more chip industry academic papers.
Name* (Note: This name will be displayed publicly)
Email* (This will not be displayed publicly)
Suppliers are investing new 300mm capacity, but it’s probably not enough. And despite burgeoning 200mm demand, only Okmetic and new players in China are adding capacity.
Why UCIe is so important for heterogeneous integration.
Funding rolls in for photonics and batteries; 88 startups raise $1.3B.
Experts at the Table: Designing for context, and geopolitical impacts on a global supply chain.
Memory footprint, speed and density scaling are compounded by low-power constraints.
Interest in this particular ISA is expanding, but the growth of other open-source hardware is less certain.
Nanosheets are likeliest option throughout this decade, with CFETs and other exotic structures possible after that.
Hybrid bonding opens up whole new level of performance in packaging, but it’s not the only improvement.
Suppliers are investing new 300mm capacity, but it’s probably not enough. And despite burgeoning 200mm demand, only Okmetic and new players in China are adding capacity.
Why this is becoming a bigger issue, and what can be done to mitigate the effects.
From low resistance vias to buried power rails, it takes multiple strategies to usher in 2nm chips.
Manufacturing 3D structures will require atomic-level control of what’s removed and what stays on a wafer.
Disaggregation and the wind-down of Moore’s Law have changed everything.