Quadric's general-purpose NPU supports KANs, which greatly reduce power consumption, improving energy efficiency. https://lnkd.in/gJPHCnuy
Quadric’s Post
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In the world of #LLMs, efficient resource utilization can make or break a fine-tuning strategy. See why LoRa and QLoRA are optimal fine-tuning approaches to consider – and how easy it is to get started! https://bit.ly/3RF42iP
Efficient Fine-Tuning with LoRA: A Guide to Optimal Parameter Selection for Large Language Models
databricks.com
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EPRI just made publicly available this tech brief on AI-assisted analysis of ultrasonic inspections. It presents our collaboration up to the first field trial late last year. This should be interesting reading to anybody planning to implement AI in a risk-sensitive environment. Key insights: • evaluate fitness-for-purpose with existing metrics specific to the use case • re-evaluate these metrics to make sure they remain valid with AI • good fit to current practices and processes is paramount • field-trials provide indispensable insight, build trust, and uncover additional benefits #AI #innovation #nde https://lnkd.in/dRPiN-BV
EPRI Home
epri.com
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This letter studies the optimization problem for minimizing the schedule length for net-zero-energy networks with short packets where the schedule length is defined as the total time duration required for the RF #energy #harvesting (#EH) in the downlink and information transmission by exhausting the harvested energy in the uplink. The problem is nonlinear and non-convex, so hard to solve. Framework is proposed with the master problem searching for optimal EH duration iteratively and subproblems of calculating the schedule length for a given EH time in each iteration. ---- Aysun G. Önalan, Sinem Coleri More details can be found at this link: https://lnkd.in/g6XEDdT4
Optimization Theory and Deep Learning Based Resource Allocation in Net-Zero-Energy Networks With Short Packets
ieeexplore.ieee.org
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Last year, we have published a paper in TWPRS, the topic is about electricity price forecast. The most fascinating part of this work is two salient, but intriguing pattern in electricity price named temporal variability and feature-wise variability https://lnkd.in/gzQKF-zv
Dense Skip Attention Based Deep Learning for Day-Ahead Electricity Price Forecasting
ieeexplore.ieee.org
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Oceanologist, BAP/BSP Market Development USA & Latin America (Aquaculture & Fisheries). #Blockchain enthusiast. I Love My Daughters =)
... #Solar #Photoelectrics & #Thermal, #Eolic blades, #Thermoelectrics, #Piezoelectrics, non-reactive #magnetic generators, thin radiation shielding/ #RTG...
AI News: Real-Time AI is HERE (and It's WILD!)
https://www.youtube.com/
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Find our new conference paper online: Abstract- The increasing penetration of solar power in power systems necessitates accurate generation forecasts to ensure reliable and economical operation. Several forecasting models like time series, machine learning, and deep learning models are used for solar generation forecasting. Deep learning models like LSTM show superior performance among these models. However, the forecasting accuracy of the LSTM model heavily relies on hyperparameters. This necessitates hyperparameter optimization using a suitable optimization algorithm. Therefore, this paper proposes a new hybrid LSTM model called LSTM_WaOA where hyperparameters are optimized using the Walrus Optimization Algorithm (WaOA). The LSTM is known for its three-layer architecture and is chosen for its effectiveness in handling and understanding long-term patterns and dependencies in data. The choice of WaOA is motivated by its proven superiority over different metaheuristic algorithms. This algorithm is crucial in optimizing hyperparameters, ensuring the LSTM model is finetuned to deliver the most efficient and accurate forecasting. The performance of the LSTM_WaOA model is evaluated by a seasonal analysis. Also, the performance is compared with traditional LSTM and ANN models. The results demonstrate that the proposed hybrid model outperforms the reference models. https://lnkd.in/gx8ct7FV
Solar Power Prediction Using LSTM_WaOA Model
ieeexplore.ieee.org
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SOLAR 10.7B from Upstage is awesome. It is highly ranked in the Open LLM leaderboard: https://lnkd.in/g4rs8r-8. If you want to run SOLAR 10.7B with high speed and low costs, then try out Friendli Suite from FriendliAI. :-) To learn more about the model, you can read their paper titled "SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling." https://lnkd.in/gEHDTbcu
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
arxiv.org
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Nice explanation of the transformers that power LLMs. https://lnkd.in/dBH7q5Rm
Generative AI exists because of the transformer
ig.ft.com
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Today’s The Washington Post highlights the US’ rising power needs, driven in part by technology, the latter including AI. I am not an AI expert, but my sense from research I do is that tech companies already see this limitation on the horizon and are actively working to find solutions. Check out Liquid AI as an example of what can be done with less computational power. https://lnkd.in/eeaGf-ns
Amid explosive demand, America is running out of power
washingtonpost.com
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Remember the TV Show “How it’s Made”? In 30 minute segments, we saw how Reese’s Peanut Butter Cups were created at automated factories as well as how mega turbines were manufactured and transported to large hydroelectric projects. This is the “How it’s Made” for Gen AI, in a short and easy to understand video.
How Chatbots and Large Language Models Work
https://www.youtube.com/
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