Recent searches
Search options
Administered by:
#thermodynamic
0 posts0 participants0 posts today
Computing Evolves: P-bits Cut AI Energy Costs
Traditional computing is transforming into something more organic, efficient, and profound.
@Entropic_Labs @normalcomputing
#Probabilistic #Thermodynamic #AI #LLM
Analog computers make a comeback for calculating Neural Networks with huge improvements in power usage and time.
Claims of 5 orders of magnitude improvement in performance 
#ai #thermodynamic #computing
https://www.future-of-computing.com/gavin-crooks-how-normal-computing-solves-linear-algebra-problems-with-thermodynamics/
Future of Computing · Gavin Crooks: How Normal Computing Solves Linear Algebra Problems With ThermodynamicsThermodynamic computers leverage the natural tendency of physical systems to minimize energy and reach equilibrium states for computing
I persist in being very angry they are calling this a #climate crisis. Then people say "The #weather's warm, you power hungry authoritarian!" and the "science sloganeers" then FALSELY sloganize "Climate is Not Weather".
No, it is, you slogan-shouting halfwit.
Where you went wrong was calling it a CLIMATE problem in the first place. It is a #THERMODYNAMIC crisis. Earth is now a net ABSORBER of solar stuff-that-moves-things (energy, capacity to do work).
(to be continued briefly)
"A thermodynamical model of non-deterministic computation in cortical neural networks"
https://www.biorxiv.org/content/10.1101/2022.12.03.518978v1
bioRxivA thermodynamical model of non-deterministic computation in cortical neural networksNeuronal populations in the cerebral cortex engage in probabilistic coding, effectively encoding the state of the surrounding environment with high accuracy and extraordinary energy efficiency. A new approach models the inherently probabilistic nature of cortical neuron signaling outcomes as a thermodynamic process of non-deterministic computation. A mean field approach is used, with the trial Hamiltonian maximizing free energy and minimizing the net quantity of temperature-entropy, compared with a reference Hamiltonian. Thermodynamic quantities are always conserved during the computation; free energy must be expended to produce information, and free energy is released during information compression, as correlations are identified between the encoding system and its surrounding environment. Due to the relationship between the Gibbs free energy equation and the Nernst equation, any increase in free energy is paired with a local decrease in membrane potential. As a result, this process of thermodynamic computation adjusts the likelihood of each neuron firing an action potential. This model shows that non-deterministic signaling outcomes can be achieved by noisy cortical neurons, through an energy-efficient computational process that involves optimally redistributing a Hamiltonian over some time evolution. Calculations demonstrate that the energy efficiency of the human brain is consistent with this model of non-deterministic computation, with net entropy production far too low to retain the assumptions of a classical system.
### Competing Interest Statement
The authors have declared no competing interest.