AI Inference: The Secret to AI's Superpowers

00:10:41
https://www.youtube.com/watch?v=XtT5i0ZeHHE

Résumé

TLDRIn the lifecycle of an AI model, inferencing is a critical stage where the model applies learned information to real-world tasks. This involves comparing new data against the information processed and encoded in the form of model weights during training. The primary goal is to produce actionable results, such as identifying spam emails based on learned patterns. However, inferencing is resource-intensive and expensive, often contributing substantially to the AI model's carbon footprint. Optimizing inferencing involves techniques like model compression, specialized hardware, and more efficient middleware systems to reduce costs and improve speed. With continuous advancements, the challenge remains in balancing the high demand for quick, accurate responses with minimizing operational expenses and environmental impact.

A retenir

  • 🧠 Inferencing applies training knowledge to new data.
  • 💰 High costs mainly stem from inferencing rather than training.
  • 📧 Spam detection is a common example of inferencing in action.
  • ⚙️ Specialized hardware speeds up inferencing tasks.
  • 🔍 Model compression helps in reducing inferencing costs.
  • 🌿 Inferencing has a significant carbon footprint impact.
  • ⚡ Need for speed makes AI models resource-intensive.
  • 🖥️ Middleware enhances hardware-software integration.
  • 🔢 Pruning and quantization optimize model efficiency.
  • 🔗 Inferencing involves intricate pattern matching.

Chronologie

  • 00:00:00 - 00:05:00

    Inferencing in AI models is the stage following training, where the model applies learned information to predict or solve tasks using real-time data. During training, the model learns relationships in its dataset and encodes them into model weights. Inference involves comparing new data with stored model weights to generalize and interpret this data, similar to humans applying past knowledge to new situations. The aim is to produce actionable results, like predicting if an email is spam, which could lead to automated actions based on the model's probability score of being spam.

  • 00:05:00 - 00:10:41

    Inferencing is costly and energy-intensive. Unlike the one-time training phase, inference occurs repeatedly throughout a model's life, using considerable computational resources and infrastructure. The frequency of inference operations and the demand for quick responses require expensive energy-hungry hardware. Larger, more complex AI models, particularly those with billions of parameters, necessitate this intensive computation. Efforts to reduce these costs include developing specialized AI chips and optimizing software through model compression techniques like pruning and quantization. These enhancements at hardware, software, and middleware levels aim to improve inferencing efficiency and speed.

Carte mentale

Mind Map

Vidéo Q&R

  • What are the two primary stages of an AI model?

    The two primary stages are the training stage, where the model learns, and the inferencing stage, where it applies knowledge to new data.

  • What does a model do during inferencing?

    During inferencing, a model applies learned weights to new, real-time data to make predictions or decisions.

  • How does a spam detection model work in the inferencing stage?

    It analyzes incoming emails in real-time, compares them to learned spam patterns, and predicts whether they are spam.

  • Why is inferencing considered the costly stage of AI?

    Inferencing occurs millions or billions of times and requires significant computational resources, leading to high costs in energy and operations.

  • What can be done to reduce inferencing costs?

    Optimizations through specialized hardware, model compression techniques, and efficient middleware can speed up and lower inferencing costs.

  • What is model compression?

    Model compression involves techniques like pruning unnecessary weights and quantizing model parameters to enhance efficiency.

  • What role does middleware play in inferencing?

    Middleware optimizes communication and parallel computation, aiding in efficient use of hardware resources.

  • How do AI accelerators compare to GPUs in inferencing?

    AI accelerators are specialized chips that perform key AI operations faster and more energy-efficiently than traditional GPUs.

  • What is the significance of hardware in AI inferencing?

    Hardware improvements, like AI-specific chips, play a crucial role in speeding up inferencing tasks.

  • What are some challenges associated with inferencing AI models?

    Challenges include the high cost, need for speed, complexity of models, and infrastructure requirements.

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  • 00:00:00
    what is inferencing it's an AI model's
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    time to shine its Moment of Truth a test
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    of how well the model can apply
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    information learned during training to
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    make a prediction or solve a task and
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    with it comes a focus on cost and speed
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    let's get into
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    it so an AI model it goes through two
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    primary stages what are those the first
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    of those is the
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    training stage where the model learns
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    how to do stuff and then we have the
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    inferencing stage that comes after
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    training now we can think of this as the
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    difference between learning something
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    and then putting what we've learned into
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    practice so during training a deep
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    learning model comput how the examples
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    in its training set are related what
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    it's doing effectively here is it's
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    figuring out
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    relationships between all of the data in
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    its training set and it encodes these
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    relationships into what are called a
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    series of model weights these are the
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    weights that connect its artificial
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    neurons so that's training now during
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    inference a model goes to work on what
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    we provide it which is real time data so
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    this is the actual data that we are
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    inputting into the
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    model what happens inferencing is the
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    model compares the user's query with the
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    information processed during training
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    and all of those stored weights and what
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    the model effectively does is it
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    generalizes based on everything that has
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    learned during training so it
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    generalizes from this storage
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    representation to be able to interpret
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    this new unseen data in much the same
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    way that you and I can draw on prior
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    knowledge to infer the meaning of a new
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    word or make sense of a new
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    situation and what's the goal of this
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    well the goal of AI inference is to
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    calculate an output basically a result
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    an actionable
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    result so what sort of result are we
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    talking about well let's consider a
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    model that attempts to accurately flag
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    incoming email and it's going to flag it
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    based on whether or not it thinks it is
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    Spam we are going to build a Spam
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    detector
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    model right so during the training stage
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    this model would be fed a large labeled
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    data set so we get in a whole load of
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    data here and this contains a bunch of
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    emails that have been labeled
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    specifically the labels are spam or not
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    spam for each email and what happens
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    here is the model learns to recognize
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    patterns and features commonly
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    associated with Spam emails so these
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    might include the presence of certain
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    keywords yeah those ones so unusual
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    sender email addresses excessive use of
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    exclamation marks all that sort of thing
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    now the model encodes these learned
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    patterns into its weight here creating a
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    complex set of rules to identify spam
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    now during inference this model is put
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    to the test it's put to the test with
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    new unseen data in real time like when a
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    new email arrives in a user's inbox the
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    model analyzes the incoming email
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    comparing its characteristics to the
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    patterns it's learned during training
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    and then makes a prediction is this new
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    unseen email spam or not spam now the
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    actionable result here might be a
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    probability score indicating How likely
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    the email is to be spam which is then
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    tied into a business rule so for example
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    if the model assigns a
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    90%
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    probability that what we're looking at
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    here is Spam well we should move that
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    email directly to the spam folder that's
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    what the business rule would say but if
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    the probability the model comes back
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    with is just
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    50% the business rule might say to leave
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    the email in the inbox but flag it for
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    the user to to decide what to do so
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    what's happening here is the model is
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    generalizing it can identify spam emails
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    even if they don't exactly match any
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    specific example from its training data
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    as long as they share similar
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    characteristics with the spam pattern
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    its learn okay now when the topic of
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    inferencing comes up it is often
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    accompanied with four preceeding words
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    let's cover those
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    next the high cost of those are the
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    words often added before inferencing
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    training AI models particularly large
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    language models can cost millions of
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    dollars in Computing processing time but
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    as expensive as training an AO model can
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    be it is
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    by the expense of inferencing each time
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    someone runs an AI model there's a cost
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    a cost in kilowatt hours a cost in
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    dollars a cost in carbon Emissions on
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    average something like about
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    90% of an AI model's life is spent in
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    inferencing mode and therefore most of
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    the ai's carbon footprint comes from
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    serving models to the world not in
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    training them in fact by some estimates
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    running a large AI model puts more
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    carbon into the atmosphere over its
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    lifetime than the average American car
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    now the high costs of inferencing they
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    stem from a number of different factors
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    so let's take a look at some of those
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    and first of all there's just the the
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    sheer scale the scale of operations
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    while training happens just once
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    inferencing happens millions or even
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    billions of times over a model's
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    lifetime a chatbot might field millions
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    of queries every day each requiring a
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    separate inference second there's the
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    need the Need for Speed we want fast AI
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    models we're working with realtime data
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    here requiring near instantaneous
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    responses which often necessitate
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    powerful energy hungry Hardware like
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    gpus third we have to consider also just
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    the general
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    complexity of these AI models as models
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    grow larger and more sophisticated to
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    handle more complex tasks they require
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    more computational resources for each
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    inference this is particularly true for
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    llms with billions of parameters and
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    then finally there is the cost in terms
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    of infrastructure costs data centers to
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    maintain and cool load latency network
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    connections to power all these factors
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    contribute to significant ongoing costs
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    in terms of energy consumption Hardware
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    wear and tear and operational expenses
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    which brings up the question of if
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    there's a better way to do this faster
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    and more
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    efficiently how fast an AI model runs
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    depends on the stack what's the stack
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    well improvements made at each layer can
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    speed up inferencing and top of the
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    stack is Hardware at the hardware level
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    Engineers are developing specialized
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    chips these these are chips made for AI
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    and they're optimized for the types of
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    mathematical operations that dominate
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    deep learning particularly matrix
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    multiplication these AI accelerators can
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    significantly speed up inferencing tasks
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    compared to traditional CPUs and even to
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    gpus and to do so in a more energy
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    efficient way now bottom of the
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    stack I've put software and on the
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    software side there are several
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    approaches to accelerate inferencing one
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    is model compression now that involves
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    techniques like pruning and quantization
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    so what do we mean by those well first
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    of all pruning that removes unnecessary
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    weights from the model so it's reducing
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    its size without significantly impacting
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    accuracy and then for quantization what
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    that is talking about is reducing the
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    Precision of the model's weights such as
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    from 32-bit floating Point numbers to 8
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    bit integers and that can really speed
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    up computations and reduce memory
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    requirements okay so we got hardware and
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    software what's in the middle middle
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    Weare of course middle Weare Bridges the
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    gap between the hardware and the
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    software and middleware Frameworks can
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    perform a bunch of things to help here
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    one of those things is called graph
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    fusion and graph Fusion reduces es the
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    number of nodes in the communication
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    graph and that minimizes the round trips
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    between CPUs and gpus and they can also
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    Implement parallel tensors as well
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    strategically splitting the AI models
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    computational graph into chunks and
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    those chunks can be spread across
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    multiple gpus and run at the same time
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    so running a 70 billion parameter model
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    that requires something like 150 GB of
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    memory which is nearly twice as much as
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    an Nvidia a100 GPU holds but if the par
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    compiler can split the AI model's
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    computational graph into strategic
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    chunks those operations can be spread
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    across gpus and run at the same time so
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    that's inferencing it's a game a game of
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    patent matching that turns complex
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    training into rapid fire problem solving
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    one spammy email at at a
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    time if you have any questions please
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    drop us a line below and if you want to
  • 00:10:34
    see more videos like this in the future
  • 00:10:36
    please like And subscribe thanks for
  • 00:10:39
    watching
Tags
  • AI inferencing
  • training
  • cost
  • efficiency
  • spam detection
  • hardware
  • middleware
  • carbon footprint
  • optimization
  • real-time data