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This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details B. Experimental Results C. Arithmetic Intensity D. Predictor Analysis 8 CONCLUSION In this work, we examine the unique characteristics of energy consumption in neural network inference, especially for edge devices. We identified unique tradeoffs and dimensions between energy consumption and inference latency SLOs and empirically demonstrated hidden components in optimizing energy consumption. We then propose an optimization framework that automatically and holistically tunes various hardware components to find a configuration aligned with the Pareto Frontier. We empirically verify the effectiveness and efficiency of PolyThrottle. PolyThrottle also adapts to the need for fine-tuning and proposes a simple performance prediction model to adaptively schedule finetuning requests while keeping the online inference workload under the inference latency SLO whenever possible. We hope our study sheds more light on the hidden dimension of NN energy optimization. REFERENCES Jetson partner solutions ebook. URL https://resources.nvidia.com/ en-us-jetson-success-stories/ jetson-partner-solutions-ebook?lx=XRDs_y. Twitter streaming traces, 2018. URL https://archive.org/details/ archiveteam-twitter-stream-201804. Alipourfard, O., Liu, H. H., Chen, J., Venkataraman, S., Yu, M., and Zhang, M. Cherrypick: Adaptively unearthing the best cloud configurations for big data analytics. In NSDI, volume 2, pp. 4–2, 2017. Anderson, T., Belay, A., Chowdhury, M., Cidon, A., and Zhang, I. Treehouse: A case for carbon-aware datacenter software. 2022. Anthony, L. F. W., Kanding, B., and Selvan, R. Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. 2020. Arafa, Y., ElWazir, A., ElKanishy, A., Aly, Y., Elsayed, A., Badawy, A., Chennupati, G., Eidenbenz, S., and Santhi, N. Verified instruction-level energy consumption measurement for nvidia gpus. 2020. Bai, Z., Zhang, Z., Zhu, Y., and Jin, X. Pipeswitch: Fast pipelined context switching for deep learning applications. In Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation, pp. 499–514, 2020. Banner, R., Hubara, I., Hoffer, E., and Soudry, D. Scalable methods for 8-bit training of neural networks. Advances in neural information processing systems, 31, 2018. Brochu, E., Cora, V. M., and De Freitas, N. A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599, 2010. 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Ernest: Efficient performance prediction for {Large-Scale} advanced analytics. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16), pp. 363–378, 2016. Wan, C., Santriaji, M., Rogers, E., Hoffmann, H., Maire, M., and Lu, S. Alert: Accurate learning for energy and timeliness. In ATC, 2020. Wang, F., Zhang, W., Lai, S., Hao, M., and Wang, Z. Dynamic gpu energy optimization for machine learning training workloads. IEEE Transactions on Parallel and Distributed Systems, 2021. Wang, G., Venkataraman, S., Phanishayee, A., Devanur, N., Thelin, J., and Stoica, I. Blink: Fast and generic collectives for distributed ml. In Proceedings of Machine Learning and Systems, 2020a. Wang, Y., Wang, Q., Shi, S., He, X., Tang, Z., Zhao, K., and Chu, X. Benchmarking the performance and energy efficiency of ai accelerators for ai training. In 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020b. 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PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Conclusion & References

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. : Authors (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details B. Experimental Results C. Arithmetic Intensity D. Predictor Analysis 5 PROBLEM FORMULATION: TWO-PHASE TUNING Our objective is to automatically find the optimal hardware configurations that minimize energy consumption while satisfying latency SLOs. Formally, we are solving the optimization problem: 1. The search space is large, and performing a grid search will take hours depending on the model size. On TX2 and Orin, there are 5005 and 1820 points in the grid if we allow 5 different batch sizes. An exhaustive search would take 14 and 5 hours, respectively. 2. To satisfy latency constraints, it is hard to decouple each dimension and optimize them separately as they jointly affect inference latency in a non-trivial way. 3. The optimization landscape may vary across models and devices. We observe that CPU frequency can be decoupled from GPU frequency, memory frequency, and batch size, as it mainly affects the preprocessing latency and energy consumption. We pipeline the requests so different requests can use CPU and GPU resources at the same time to increase inference throughput. Based on this observation, we propose a two-phase hardware tuning framework, where CPU tuning is done separately from tuning other hardware components. The challenge that remains is to efficiently optimize for an unknown function with noise. As shown in Figure 3 and 4, the performance of neural network inference with changes in memory and GPU frequency is difficult to predict, therefore, a good solution must be able to handle the variance while converging to a near-optimal configuration in a sample-efficient fashion. This requires the method to adaptively balance the tradeoff between exploration and exploitation. To solve this, we formulate the optimization problem as a Bayesian Optimization problem and leverage recent advances in the field to incorporate the SLO constraints unique to our setting. 5.1 Constrained Bayesian Optimization Bayesian Optimization is a prevalent method for hyperparameter tuning (Kandasamy et al., 2020; Klein et al., 2015), as it can optimize various black-box functions. This method is especially advantageous when evaluating the objective function is expensive and requires a substantial amount of time and resources. However, some applications may involve constraints that must be satisfied in addition to optimizing the objective function. Constrained Bayesian Optimization (CBO) (Gardner et al., 2014) is an extension of Bayesian Optimization that tackles this challenge by incorporating constraints into the optimization process. In CBO, the objective function and constraints are treated as distinct functions. The optimization algorithm seeks to identify the set of input parameters that maximize the objective function while adhering to the constraints. These constraints are usually expressed as inequality constraints that must be satisfied during the optimization process. The expected constrained improvement acquisition function in CBO is defined as follows: EIC (ˆx) = P F(ˆx) × EI(ˆx). Here EI(ˆx) represents the expected improvement (EI) (Brochu et al., 2010) within an unconstrained Bayesian Optimization scenario, while P F(ˆx) is a univariate Gaussian cumulative distribution function, delineating the anticipated probability of whether xˆ can fulfill the constraints. Intuitively, EI chooses the next configuration by optimizing the expected improvement relative to the best recently explored configuration. In PolyThrottle, we choose EI since our empirical findings and corroborations from additional studies (Alipourfard et al., 2017) show that EI performs better than other widely-used acquisition functions (Snoek et al., 2012). CBO (Gardner et al., 2014) also employs a joint prior distribution over the objective and constraint functions that captures their correlation structure. This joint prior is constructed by assuming that the objective and constraint functions are drawn from a multivariate Gaussian distribution with a parameterized mean vector and covariance matrix. These hyperparameters are learned from data using maximum likelihood estimation. During the optimization process, the algorithm uses this joint prior to compute an acquisition function that balances exploration (sampling points with high uncertainty) and exploitation (sampling points where the objective function is expected to be low and subject to feasibility constraints). The algorithm then selects the next point to evaluate based on this acquisition function. During each iteration, the algorithm will test whether the selected configuration violates any of the given constraints and take the result into account for the next iteration. Encoding more system-specific hints as constraints can be of independent research interests, however, we show in Section 7 that the current formulation performs well under a variety of scenarios.

Proble Formulation: Two-Phase Tuning

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Prompting and prompt engineering are easily the most in demand skills of 2023. The rapid growth of LLMs has only seen an emergence of this new discipline of AI called prompt engineering. In this article, lets take a brief look at what prompting is, what prompt engineers do and also the different elements of a prompt that a prompt engineer works with. Large Language Models What exactly is a prompt? Prompt is simply the input that you provide to a “trained” model. When I say trained model, the weights of the model are fixed or frozen and are not going to change during the prompting process. You may now ask how is it different from inference because we have been training models, deploying and running inference on on machine learning models. The point is that with inference, the input is fixed. We never ever change it and whatever the model gives as output, we accept it as the result. Think of image classification as an example task. However, with prompting, you are not restricted to a single input. You can tweak the input to your needs to improve the model’s behaviour. You are more of instructing the model with the intention to steer it the the right answer. While you accept the model for given when it comes to inference, with prompting, you are studying the capabilities and limitations of the model. The art of designing or engineering these inputs to suit the problem on hand so as to serve you best gives birth to a fairly new discipline called . prompt engineering Prompt Engineering Before diving into prompt engineering, lets understand the motivation or need to engineer prompts with examples. Lets say I want to summarise a given passage. So I give a big passage from wikipedia as input and in the end say, “ ”. This way of providing simple instructions in the prompt to get an answer from the LLM is known as . summarise the above paragraph Instruction prompting Lets move to a slightly more complicated case of mathematics and ask the LLM to multiply two numbers. Lets try the prompt, “ The answer we get is “ ”, which obviously is not the multiplication of the two numbers but both put together. what is 2343*1232”. 23431232 Now let me modify the prompt and add an extra line in the prompt to be more specific, “ ”. We now get, “ ” the right answer from the LLM. what is 2343 multiplied by 1232. Give me the exact answer after multiplication 2886576 So, clearly the quality of model’s output is determined by the quality of the prompt. This is where prompt engineering comes into play. The goal of a prompt engineer is to and identify areas of improvement in the prompt to get better outputs. So prompt engineering is a by trial and error with the intention of both understanding the LLM and also designing good prompts. assess the quality of output from a model highly experimental discipline of studying the capabilities and limitations of the LLM Prompt elements In order to engineer or design the prompts, we have to understand the different elements of a prompt. A prompt can contain any one or more of the following elements. Prompts can be where you ask the model to do something. In our example, we provided a huge body of text and asked the model to summarise it. instructions Prompts can optionally include a for the model to better serve you. For example, if I have questions about say, English heritage sites, I can first provide a context like, “English Heritage cares for over 400 historic monuments, buildings and places — from world-famous prehistoric sites to grand medieval castles, from Roman forts … “ and then ask my question as, “ ” context which is the largest English heritage site? As part of the prompt, you can also instruct on the format in which you wish to see the output. And so a prompt can optionally have an . For example, you can ask “ . .” output indicator I want a list of all the English heritage sites in England, their location and specialty I want the results in tabular format Or if you want even better response, you can enter the desired format with this syntax to indicate you wish to see columns and rows in the output: Desired format: Company names: <comma_separated_list_of_sites> Sites: -||-Location: -||-Speciality: -||- A prompt can include one or more where we provide example inputs for what is expected from the model. In the case of sentiment classification, take a look at this prompt where we start providing examples to show our intentions and also specify we don’t want any explanation in the response: input data Text: Today I saw a movie. It was amazing. sentiment: Positive Text: I don't very good after seeing that incident. sentiment: Types of Prompts — shots This way of giving examples in the prompt is similar to how we explain to humans by showing examples. In the prompting world its called . We provide high quality examples having both the input and the output of the task. This way the model understands what you are after and so responds far better. few-shot prompting Expanding on our example, if I want to know the sentiment of a passage, instead of just asking, “what is the sentiment of the passage”, I can provide a few example covering the possible classes in the output. In this case positive and negative: Text: Today I saw a movie. It was amazing. sentiment: Positive Text: I don't very good after seeing that incident. sentiment: Negative Text: Lets party this weekend to celebrate your anniversary. sentiment: Positive Text: Walking in that neighbourhood is quite dangerous.sentiment: Negative Text: I love watching tennis all day long sentiment: And I can then leave the model to respond to the last text I entered. Typically 5 to 8 examples should be good enough for few-shot prompting. As you can guess by now, the drawback of this approach being that there will be too many tokens in your prompt. If you wish to start simple, you need not provide any examples, but jump straight to the problem like this prompt: Text: I love watching tennis all day long sentimet: This is where you don’t provide any examples but still expect the model to answer you properly. Typically while prompt engineering, you start with zero-shots as its simpler and based on the response, you move on to few-shots by providing examples to get better response. zero-shot prompting Types of Prompts — roles If you wish to jump to a specialised topic with the LLM, you can straightaway steer it to be an expert in a field by assigning it a role and this is called . role prompting You would typically start the prompt with the expert role the LLM has to play. Then follow with the instructions for what it needs to do. As a simple example, the role could be asking the LLM to be a poet and the instruction could be to just write a poem about AI Bites. Or it could be slightly more complicated by asking the LLM to act as a terminal. And providing specific instructions to copy the first 10 lines of a file into a different file and to save it. You can even prevent it from including any other text in the output by explicitly mentioning not to give any explanation. linux You are a poet. Write a poem about AI Bites Act as a linux terminalI want you to provide the shell command to read the contents of a file named "input.txt".Copy the first 10 lines to a different file with the name "new.txt" and save it.Do not give any explanations. A formal Prompt structure Different possible elements of a prompt we can input to a LLM With all that said, if you want me to formalize the structure of a prompt, I would go about like this. A prompt typically starts with a role which the model has to play if your prompt is about a specialised topic. Then it can have any instructions you would like to give the LLM. On top of that if you wish to provide additional information to the LLM, it can also go after the instruction. Soon after that you can provide high quality examples if you are doing few-shot prompting. These examples can then be followed by any context you wish to provide to the model. If you wish to ask a question and do a q and A task, you can include your questions in the end Prompt Formatting Now that we have seen what constitutes a prompt, its even better if we know how to format these prompts. For example, its better to explicitly mention desired format and then actually provide the format. Extract locations from the below textDesired format:Cities: <comma_separated_list_of_cities>Countries: <comma_separated_list_of_countries>Input: Although the exact age of Aleppo in Syria is unknown,an ancient temple discovered in the city dates to around 3,000 B.C. Excavations in the1990s unearthed evidence of 5,000 years of civilization,dating Beirut, which is now Lebanon's capital, to around 3,000 B.C. Similarly for input or context its better to say input followed by a colon and then provide your input. When providing examples it is better to separate them with a couple of hashes like in this example. Text: Today I saw a movie. It was amazing.sentiment: Positive Text: I don't very good after seeing that incident.sentiment: Negative Text: Lets party this weekend to celebrate your anniversary.sentiment: PositiveText: Walking in that neighbourhood is quite dangerous.sentiment: Negative If you are providing input, you may wrap it in quotes like this example: Text: """{text input here}""" Then there is something called the which hints the model to stop churning out text because its finished with the output. You may choose a stop sequence with any symbol of your choice. But new line seems to be the usual option here. stop sequence Text: "Banana", Output: "yellow \\n" Text: "Tomato", Output: "red \\n" Text: "Apple", Output: "red \\n" If you are working with say code generation, its better to provide comments according to the language you wish to see the output code to be generated in. /* Get the name of the use as input and print it */ # get the name of the user as input and print it Watch the video If you have read this far, I assume you either loved the article or are curious about AI. Eitherways, why don’t you watch our video on Prompt Engineering . This is the first video in the series of videos about Prompting. So why not subscribe and stay tuned! here https://youtu.be/zS6JtmVRpo8?embedable=true Conclusion With all that introduction about prompts, prompt engineering and their types, we have only scratched the surface here. For example, There are more advanced ways to prompt like chain-of-thought, self-consistency, general knowledge, etc. Lets have a look at those in the upcoming posts and videos. Please stay tuned! how can we ask the LLM to reason about a given situation?

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