AI Semiconductors: Trends, Challenges, and Future Directions

Sungtae, the community manager at Model Research Institute, introduces Mr. Dongsoo Lee, an expert in AI semiconductors, for a seminar on AI semiconductor ecosystem.

### Naver Cloud AI Semiconductor Strategy and Trends
* 🌐 The Model Research Institute has invited Mr. Dongsoo Lee from Naver Cloud to give a seminar on **understanding and participating in the AI semiconductor ecosystem**.
* 🏫 The Model Research Institute is an educational company focusing on AI technology and research platforms, offering various programs with a mission to **spread a research culture of sharing knowledge and growing together**.
* 🔬 The programs are divided into three categories: **short-term, medium-term, and long-term**, with offerings like **Lab, Paper Shop, and Flip School**.
* 🚀 NVIDIA is a key player in AI semiconductors, excelling in **matrix operations**, and **transformers** dominate the field.
* 💡 Lightweighting is a crucial aspect of AI models, leveraging **redundancies in learning** to **compress models**.
* 🛠 Optimizing ecosystem software parts related to transformers is becoming important in the **AI semiconductor space**.
* 📈 As models grow larger, memory and chip-to-chip communication become critical bottlenecks, leading to **disproportionately higher costs**.
* 📅 The trend is towards **increasingly larger transformer models**, with **memory size and bandwidth steadily improving**.
* 🔒 NVIDIA's leadership in implementing **high-bandwidth memory (HBM)** on chips is a **significant advantage**.
* 📊 Microsoft's recent research on optimizing GPU resource usage for large models like GPT-3 highlights the importance of **cost reduction strategies**.
* ⚡ Throughput and latency are key factors in reducing costs, with **batch processing often used to increase throughput**.

### Strategies for Overcoming Challenges in the AI Semiconductor Market
* 🏋 "As the batch size increases, there are cases where latency becomes a problem, and **reducing latency is always the most challenging**."
* 💡 Lightweight models are starting to emerge as a solution to **reduce latency**.
* 💰 Efforts are being made to **reduce costs** in the AI semiconductor market.
* 🏆 "NVIDIA dominates the influencer market, accounting for about **74% of the revenue**."
* With the advent of the Transformer in the AI semiconductor architecture, it is necessary to pay attention to how the ecosystem is changing, not only in terms of **hardware** but also in terms of **software**.
* 📈 "The software trend is shifting towards the **System-on-Chip (SoC) paradigm**."
* 🧠 "The success or failure of AI in the future depends on the **correct predictions**."
* 🛠 Model compression is a key strategy for **lightweighting AI models**.

### Strategies for Developing AI Semiconductors and Lightweighting Technologies
* 🏗 Intel is good at digital signal processing, and it's hard to adjust the **activation interpal**, which is why there are chips that don't use Intel.
* 💡 "In the future, it's expected that **lightweighting technologies** will become **more important** in the AI semiconductor market."
* 📊 "The cost issues are becoming very serious for each company, and there is a **great demand for chips that need to be optimized differently for each company**."
* 🧠 "One strategy is to focus on **specialized areas of AI and semiconductors** that can have the **most impact**."
* 🔒 "There are many difficult parts to talk about due to the issues in data center operations, which are considered **trade secrets**."
* 📈 "It's crucial to continue developing **AI semiconductors** and **lightweighting technologies** to open up **new markets**."
* 💬 "Significant things may happen when people who communicate well come together, and experiences like blind dates can be the basis for creating **something significant**."
* 🌐 "It's important to have an active community where many opinions can be shared, as these aspects will play a **significant role in the development of AI in the country**."

[MODUPOP] AI 반도체 생태계의 이해와 참여 방법

Share Value, Grow Together
누구나 함께 지식을 공유하고 성장하는 연구 문화를 전파합니다


모두의연구소

Seminar on AI Semiconductor Ecosystem by Mr.

The Model Research Institute is hosting a seminar on the AI semiconductor ecosystem presented by Mr.

The institute offers various educational programs focused on AI technology and research, including project-centered labs and study-centered Flip School.

Lee will discuss the crucial role of AI semiconductors in influencing artificial intelligence performance, drawing from his extensive experience in the semiconductor industry and optimization of AI models.

The Significance of AI Semiconductors and the Dominance of Transformers

NVIDIA's dominance in matrix operations makes it crucial for AI semiconductors, while the rise of the Transformer model has led to a shift in the semiconductor industry.

The importance of excelling in lightweighting for training parameters offers a significant advantage, leading to theoretical basis for achieving high compression in AI semiconductors.

The dominance of the Transformer model has revolutionized the semiconductor industry, impacting the development and differentiation of AI semiconductor companies.

The Impact of Transformer Models on Learning and Performance Improvement

Transformer models, especially GPT, are driving the scalability and performance improvement of AI models, leading to significantly larger model sizes and super-scalability.

The increasing model size and performance come with a significantly higher cost, as the price escalates disproportionately to the model's growth.

The use of multiple chips and the need for larger memory size to handle the growing models raise concerns about communication costs and speed, impacting the overall cost and efficiency of the technology.

NVIDIA's high-end chips are expensive but ultimately cost-effective due to their superior performance and power efficiency.

The increasing power consumption and memory bandwidth of AI chips are posing challenges for data center construction and cooling systems.

The balance between power consumption and memory bandwidth is crucial in maximizing chip performance and cost-effectiveness, leading to concerns and potential discontinuation of certain chip models.

NVIDIA's partnership has led to a 30% increase in GPU usage, causing serious problems with data processing.

Microsoft's Splitware paper, comparing BERT and H-100, highlights the significant cost difference and performance improvement.

The use of batch processing to increase throughput in GPU services is crucial, but it also raises concerns about latency and customer complaints.

The Impact of AI Model Size on Costs and Semiconductor Dominance

The cost of fine-tuning different AI models varies significantly, with larger models such as GPT-4 costing much more than GPT 3.

NVIDIA is dominating the AI semiconductor market, with a 74% revenue share and is crucial for handling larger AI models.

As AI models grow in size, there is an increasing need for more powerful chips to handle memory capacity and reduce latency.

The Role of TPU in AI Semiconductor Chips

TPU, first introduced in 2016, is designed specifically for matrix operations in AI.

The latest TPU version 4 shows a significant drop in power consumption, leading to the need for liquid cooling in data centers.

In contrast to other companies focusing on RAM, TPU takes a different approach by prioritizing on-chip memory and maximizing throughput for matrix operations.

The Importance and Challenges of Analog Semiconductors

Analog semiconductor research is fundamental but challenging for commercialization due to inconsistent chip performance and high production costs.

Despite the difficulties, optimization of hardware, as demonstrated by companies like Tesla and SenseTime, is crucial for the success of AI semiconductors.

The emergence of the System-on-Chip paradigm, driven by technologies like transformers, has significantly impacted the way software is developed, reducing the need for creating numerous models frequently.

The Impact of Transformers on the Learning Ecosystem

Transformers and their related structures are widely adopted in the learning ecosystem, with a significant focus on hardware cost and efficiency.

The use of Flash Attention and other emerging functions is shaping the software trend, with a shift towards L&M investment and rapid software development.

There is a growing competition and influence from startups and universities, particularly UC Berkeley, in contrast to NVIDIA's dominance, leading to a deeper focus on core functions and the need for collaboration in the software ecosystem.

The Importance of Lightweighting and Compression in AI Algorithms

AI developers can now easily upload their work as open-source through KAIST, allowing for further implementation and progress.

Intel products provide users with immediate access to compatible software, and researchers can experiment with KAIST AI paper code using a Gaudi-based system.

Proper lightweight algorithms have not been implemented in hardware yet, leading to challenges in AI algorithm compression and parallelism.

Sparse quantization and batch size adjustments have shown significant increases in compression rate and speed, making lightweighting essential for improved performance in AI algorithms.

The Impact of Lightweight Compression Algorithms on Semiconductors and AI Development

A team is confident in their AI capabilities and has patented fundamental technologies, aiming to outperform competitors.

They are focused on improving 3:2:4 compression algorithms for semiconductors, with the goal of significantly reducing costs.

The team is grappling with the challenge of integrating lightweight methods into semiconductors, facing difficulties with activation quantization and GPU-friendly quantization.

The Importance of AI Semiconductors in the Future

AI semiconductors are projected to play a crucial role in the future and require extensive use and knowledge for development.

The growing demand for AI-related hardware engineers is driven by the high costs and the need for optimized chips for different AI models.

Naver provides a conducive environment for active discussions and quick decision-making in research areas, which is highly satisfying for the AI semiconductor industry.

The Growing Intersection of AI and Semiconductors

AI and semiconductor fields are increasingly overlapping, with a focus on specialized areas like lightweighting and memory optimization.

Compression studies and benchmarking are becoming crucial in demonstrating the effectiveness of AI model compression methods.

Collaboration between companies like Intel and Samsung is driving software development and performance improvements in the semiconductor industry.

The Future of Lightweighting in AI and Semiconductor Technology

AI-based floating point unit calculations require extensive and complex algorithms, leading to the need for significant lightweighting.

The high cost of creating a B2C model is driving the importance of developing lightweight AI models to improve efficiency.

The development of semiconductor compression technology and lightweight technology is crucial for the future commercialization and market growth of AI semiconductors.

NVIDIA's New GPU Strategy and Its Impact on AI Semiconductor Startups

NVIDIA is releasing customized chips and focusing on customer interaction, which is highly praised for its communication and customer-driven approach.

The company's strategy of introducing GPUs with more efficient specs and tiered pricing may pose a challenge for other AI semiconductor startups to compete, especially in the video sector.

The growing AI market and the increasing need for AI chips due to the replacement of existing workloads create opportunities for startups to explore new directions and technologies, such as ARM-based CPUs and DPUs.

TensTorrent's Designing of AI Chips Based on RISC-V

TensTorrent is making efforts to reduce costs in architecture through measures like KB cache and compression.

The company's focus on creating more lightweight and cost-effective AI chips is leading to improved performance.

Despite the trend of focusing too much on trends in AI research, TensTorrent recommends combining different areas of expertise for differentiation.