.png){kind=icon}
Return
Self-timed Reinforcement Learning using Tsetlin Machine
.png){kind=icon}
OA
AI
DOI：10.1109/ASYNC48570.2021.00014.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a hardware design for the learning datapath of the Tsetlin machine algorithm, along with a latency analysis of the inference datapath. In order to generate a low energy hardware which is suitable for pervasive artificial intelligence applications, we use a mixture of asynchronous design techniques including Petri nets, signal transition graphs, dual-rail and bundled-data. The work builds on previous design of the inference hardware, and includes an in-depth breakdown of the automaton feedback, probability generation and Tsetlin automata. Results illustrate the advantages of asynchronous design in applications such as personalized healthcare and battery-powered internet of things devices, where energy is limited and latency is an important figure of merit. Challenges of static timing analysis in asynchronous circuits are also addressed.
Journal
I
IF:
0
Papers:
1
Citations:
0
Organization
Cited Papers
ShareSaveLearning automata based energy-efficient AI hardware design for IoT applications.png)
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
OAAI
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCESIF3.7
OAAIShareSaveShareSaveShareSave no more
no more