FELIPE OLIVEIRA

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FELIPE OLIVEIRA

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AI Hardware in a Lightweight

 

Introduction

 

In 2022 we saw a bunch of trends rise up in the AI field, such as large language models pushing the SOTA results, the born of diffusion models capable of generating stunning images text-to-image, and close to the end of the year the mind-bogglingly rising of ChatGPT a bigger model which surpasses expectations and turns the world to down head. However, a less popular trend and releases happening in AI hardware research as well as new technologies and breakthroughs, a concern on making hardware more scalable for AI is the ground for those big models to come true. 

AI hardware is a definition of narrowing down specific hardware that aims to accelerate AI development, as long as from speeds up training models until it optimizes inference performance itself, indeed AI hardware is a class of specialized hardware accelerator or computer system designed to accelerate machine learning applications, is totally needful have an AI accelerator to train a huge machine learning models, it’s like a bridge equalize by two rocks, this combination is ML huge model + AI hardware accelerator. 

 

 

 

 

The need for AI accelerators nowadays

 

Nevertheless not so discussed AI accelerators, which manage a crucial part of these giant models supporting an endlessly of parameters to speed up the training, is worth pointing out the metric of “efficiency” when comparing general purpose VS specialized hardware on AI.

 

image source: my own

This a trade-off on having more power in Efficiency executing in a general-purpose machine, it’s like a computer server for instance against a GPU server or a cluster of GPUs running a machine learning application, even though it’s possible to scale servers the key of efficiency is an accelerator which endures the training needs behind the hoods is the core of AI hardware. 

Worth to highlight isn’t just a core as CPU or GPU which leads a better performance, we can categorize it into four different classes of components presents in AI hardware.

 

  • Compute: Accelerators for Parallel Processing such as GPUs and FPGAs 
  • Memory: High-bandwidth memory and On-chip memory (SRAM3)
  • Storage: dedicated storage for large models and artifacts 
  • Networking: Infrastructure to connect multiple data centers

 

These four components point to trends in hardware now and in a near future, blending together these ones in search of a “perfect combination” of the best hardware to achieve awesome results in performance, a chip specialized to do parallel processing with a huge quantity of memory available and storage which enables the scale to keep these models and your dependencies is a Holy grail to chase. So upon what we’ve discussed so far, is important we look to statistics on this market for the next years. 

 

image source: Precedence Research

 

This research was made by Precedence Research which addresses forecasting the hardware market around AI in the next decade, in 2021 the market was valued at around $10.41 billion the projection shows till 2030 in less than 10 years market will be valued at around $89.22 billion, is totally mind-boggling to expect this about this market, but when we saw to the imminent needed to scale deep learning systems we get it by ourselves.

To anyone who wanna endeavor in AI but doesn’t know where the path chase is, it was shown that AI hardware has plenty of opportunities to do it, we have some shortages not solved yet, so dealing with large language models in real-time inference, regards to computer vision has a need to storage amount of data to training a model, or maybe the solutions could be a tiny chip that allows doing both training and inference, even storage issues on keeping huge datasets and serving data in production afterward, theses are some questions and open-ended research areas and endeavor opportunities to be explored on AI hardware.

 

 

 

 

Kind of AI Accelerators

 

Though we discussed a bit in the past topic of AI accelerators I would like to deep dive more into accelerators, then to define AI accelerators we could call them a simple way as a specialized chip aims to accelerate computing performance on deep learning training and inference, somewhat of a specialized component designed to accelerate machine learning applications speed up running on top of that, they are generally manycore and low-precision arithmetic. Since deep learning workloads rose in, came up the need to create accelerators to speed up deep learning tasks, thus a need to explore new alternatives of hardware to accelerate deep learning as well as a challenge of designing a new component to address it.

 

 

 

GPU

 

Let’s see first the most famous one the GPUs (Graphics Processing Units), pretty sure GPUs actually are the most used AI accelerator in most cases, built to support computer graphics GPUs on personal computers they got another movement to the AI field, worth the way GPUs works and try it in deep learning and as you can guess what the results were good. 

The idea to use GPUs applied upon deep learning models sums up in were designed to deal with the same type of computations required by neural networks, such as matrix multiplications so this type of computation could be executed in parallel, which allows GPUs to perform much faster than traditional CPUs. 

 

image source: my own

 

Additionally, another aspect that turns GPUs more suitable to use in deep learning is a CUDA (Compute Unified Device Architecture) core that brings a more performance during training execution, CUDA was made up of NVIDIA is a parallel computing platform and programming model, designed aimed to perform mathematical calculations this makes them more suited for matrix multiplication and faster, also optimized for memory access patterns and data transfer, which are critical for efficient performance in deep learning tasks. 

In Summary, GPUs are more suited to deep learning because provide an efficient performance under mathematical calculations in parallel than CPUs architecture, even pointing out which NVIDIA is the leading in the GPUs market. 

 

 

 

 

FPGA 

 

The FPGA (Field-Programmable Gate Array) is a type of circuit that can be configured to perform a variety of digital logical functions, it is designed to perform a specific function and cannot be reprogrammable, the FPGA chips are easier to evolve hardware, frameworks, and software and becoming popular around deep learning by your capability to perform complex computations with high throughput and low latency. 

 

image source: digikey

 

FPGAs can be programmed to implement custom logic circuits that are optimized to deep learning algorithms, they are low-power-energy consuming which ables skimp a bunch part of money if we think to train large models, by the way, FPGAs compared to GPUs are even less energy consumption than GPUs, some advantages on FPGAs are: 

 

  1. Flexibility: FPGAs can be reprogrammed to perform different functions, making them more flexible than traditional ASICs.
  2. Customizable: FPGAs can be configured to implement custom logic circuits that are optimized for deep learning algorithms, allowing them to perform these algorithms faster and more efficiently than general-purpose CPUs and GPUs.
  3. Low Latency: FPGAs can perform complex computations with low latency, which is important for real-time applications such as image and video processing.
  4. Power Efficient: FPGAs are more power-efficient than GPUs, which makes them a good alternative for edge and mobile devices, where power consumption is a concern.
  5. High Throughput: FPGAs can perform many calculations in parallel which allows them to achieve high throughput.

 

 

 

 

TPUs

 

Even though a range of accelerators use different technics to improve efficiency in deep learning systems, Google accounted for the creation of TPU (Tensor Processing Unit) The development of TPUs was led by a team in Google Brain, and they were announced in May of 2016 and used to accelerate the training and inference on Google’s internal system, Then Google has made TPUs available to developers and researchers through google cloud. (ChatGPT)

 

image source: TPU article 

Although TPUs has made since 2016 we already have some versions of new units coming out in the market, the latest one is a fourth generation which was released in 2021 TPUv4 was improved performance 2x over the v3, and a single pod in 4,096 v4 chips and each pod has 10x the interconnect bandwidth per chip at scale, compared to any other networking technology, last but not least google has another one called Edge TPU purpose to target edged computing applications such as embedded systems, meaning it is smaller and consumes less power than TPUs hosted by Google datacenters.

 

 

 

What is next ? 

 

The future of AI hardware is a pathway going actually all time there’s something new coming up, chips specialized in machine learning workloads made by research labs in the industry companies to solve a particular problem and turns out a benchmarking doing a comparison of each one, NVIDIA keeps progress on developing most robust, powerful and less consume GPUs year by year, looking forward what expect is to see more powerful GPUs enabling scale machine learning systems in the more performative way in multi-distributed, so there are advancements on frameworks like TensorFlow and PyTorch whose improve how to deal with multi-distributed training and it is affecting directly a hardware landscape, on the other hand, we can expect to see more “proprietary accelerators” being developed by their own, sucha as chip M1 crafted by Apple which aimed to accelerate machine learning at macbooks, it was a breakthrough at edge computing, tendency lead to see more of optimized chips. 

 

 

 

Conclusion

 

An overview of the entire discussion of AI accelerators, the differences between them, and what to expect in the future around AI hardware lead us to think how much important AI software and AI hardware needs to walk holding hands on progress toward future soon breakthroughs, is inversely proportional to each other, fastest advancements hadn’t seen in all ages of AI, lead hardware development accelerate as well, I wrote this article in a very sums up the way to give you a little piece of cake on AI hardware, to clarify how accelerators works, typos, pros and cons, I hope to see more researchers taking a look at this area enabling scale AI as most as possible, what are the benefits? a real-world impact in lives through AI systems with the boundless capacity to be better, growing aligned on human values (BTW another post coming out on alignment). I hope you enjoyed this post and had a piece overview of the AI hardware, let me know your opinion in the comment below, see you soon. 

 

 

 

 

References 

[1] Definition of AI accelerator

[2] AI hardware projection

[3] All about AI accelerators

[4] How does this work ?

 

 

 

 

 

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Felipe Oliveira

Felipe Oliveira

FELIPE OLIVEIRA

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