Artificial Intelligence bottlenecks – what is important to know

The field of Artificial Intelligence (AI) is currently facing several significant bottlenecks that span across technical, organizational, and strategic dimensions. One of the critical challenges anticipated for 2025-2026 is the increasing demand for high-quality, high-speed memory chips. In this context, we offer our insights into potential solutions to these bottlenecks and highlight the emerging leaders in the AI industry.

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Artificial Intelligence Bottlenecks

As of today, we would like to highlight some notable bottlenecks and challenges in the field of artificial intelligence (AI) which may affect future earnings of the leading AI companies:

Data and Computational Limitations:

The Problems:

One major bottleneck is the increasingly high demand for computational power to train large AI models. This is exacerbated by the scarcity of advanced hardware and the inefficiencies in current AI accelerator systems. For instance, according to Neureality latest research report, the CPU hosts that support these systems are often a significant performance bottleneck in AI inference processes.

In the years 2025-2026, we foresee several potential issues related to AI inference performance in CPU hosts with accelerator systems:

Memory Bottlenecks:

The “memory bottleneck ” in artificial intelligence (AI) refers to the performance limitations resulting from the disparity between the speed of data processing units (like CPUs and GPUs) and the speed of memory access. Essentially, the processor can perform operations much faster than data can be moved to and from memory, leading to delays and inefficiencies in AI systems.

This issue is particularly pronounced in AI applications that require large amounts of data to be processed quickly. As AI models become more complex and data-intensive, the gap between processing speed and memory access speed widens, creating a “bottleneck” that hampers overall system performance. This bottleneck is often linked to the “von Neumann bottleneck,” which describes a similar limitation in traditional computing architectures where the rate of data transfer between the CPU and memory is slower than the rate at which the CPU can process data.

As AI models become more complex and data-intensive, memory bandwidth can become a limiting factor.

Competition and Innovation:

The AI accelerator market is highly competitive, with various chipmakers vying for dominance. As more players enter the field, we can expect rapid advancements, but also potential fragmentation and compatibility challenges.

There are several potential compatibility challenges that we see:

1. Endurance and Reliability: Some emerging memories have limited endurance (write cycles) compared to traditional memories like NAND flash or DRAM. Ensuring long-term reliability remains a concern.
2. Integration with Existing Systems: Integrating new memory technologies into existing hardware and software ecosystems can be complex. Compatibility issues and driver support need careful consideration.
3. Standardization: Lack of industry-wide standards can hinder adoption. Standardizing interfaces, protocols, and APIs is essential for seamless integration.
4. Energy Efficiency: While some emerging memories are energy-efficient, others may not meet the stringent power requirements of AI edge devices.
5. Cost: Early-stage technologies often come with higher costs due to low production volumes. Achieving cost parity with existing solutions is crucial.

Software Optimization:

Efficient software frameworks and libraries are crucial for maximizing AI inference performance. Ensuring that software is well-optimized for specific hardware configurations remains an ongoing challenge.

The Solutions:

Efforts to mitigate the memory bottleneck include developing new memory technologies, optimizing data movement, and designing specialized hardware to better handle AI workloads. These solutions aim to improve the efficiency of data transfer and storage, thereby enhancing the performance of AI systems.

Researchers and organizations are actively working on addressing the challenges associated with emerging memory technologies. Below we highlight some notable initiatives:

Industry Consortia and Collaborations:

Groups like the?Non-Volatile Memory Express (NVMe) Consortium?and the?JEDEC Solid State Technology Association?collaborate to develop standards and promote adoption. Their efforts help streamline integration and ensure compatibility across devices.

Universities and Research Labs:

Academic institutions worldwide conduct research on novel memory technologies. They explore materials science, device physics, and system-level optimizations. For example,?Stanford University,?MIT, and?UC Berkeley?have ongoing projects related to emerging memories.

Government Funding:

Governments invest in research through grants and programs. For instance, the?European Union’s Horizon 2020?initiative supports projects related to advanced memory technologies.

Industry-Driven Research:

Companies like?Intel,?Samsung, and?IBM?allocate resources to R&D. They collaborate with universities and contribute to open-source projects. For example, Intel’s?Optane?memory technology is a result of extensive research efforts.

Startups and Innovators:

Smaller companies and startups play a crucial role. They focus on niche solutions, exploring unconventional materials and designs. Some startups specialize in specific memory types, such as?Crossbar?(ReRAM) and?Adesto Technologies?(CBRAM).

Optimizing software to enhance AI inference performance involves several strategies and techniques:

1. Model Distillation: This process involves training a smaller model (student) to mimic the behavior of a larger, more complex model (teacher). The student model, being simpler, can perform inferences more quickly while retaining a high level of accuracy. This approach is particularly useful for deploying models on devices with limited computational resources.
2. Pruning: Pruning involves removing redundant or less important parameters from the neural network. This reduces the model size and computational load, thereby speeding up the inference process. Pruning can be done at various stages, including during training or post-training.
3. Quantization: This technique reduces the precision of the numbers used to represent the model weights, typically from 32-bit floating point to 8-bit integers. This not only reduces the model size but also speeds up the computation by allowing the use of optimized hardware instructions.
4. Neural Architecture Search (NAS): NAS automates the design of neural network architectures, optimizing them for specific tasks and hardware constraints. This can lead to more efficient models that perform better during inference.
5. Utilizing Hardware Acceleration: Leveraging specialized hardware like GPUs, TPUs, or NPUs can significantly enhance inference performance. These processors are optimized for the types of operations used in neural networks, leading to faster computation times.
6. Model Parallelism and Batch Processing: Distributing the computation across multiple processors or processing inputs in batches can also help in reducing the inference time. This approach is particularly effective when dealing with large models or high-throughput requirements.
7. Using Optimized Libraries and Frameworks: Tools such as TensorRT, ONNX Runtime, and Intel’s OpenVINO provide optimized implementations of common machine learning operations. These libraries can significantly speed up inference by taking advantage of hardware-specific optimizations. Source: NVIDIA Developer .

By combining these strategies, one can achieve significant improvements in AI inference performance, making AI applications more efficient and responsive.

Regulatory and Ethical Concerns:

The Problem:

Society, regulatory and ethical issues

As AI systems become more integrated into various aspects of society, regulatory and ethical issues are becoming more pronounced. There is a growing need to address data privacy concerns, especially with the proliferation of cloud-based AI applications.

The Solution:

Addressing regulatory and ethical concerns in the Artificial Intelligence (AI) industry is crucial for responsible development and deployment. Here are some possible solutions:

Combat Bias:

• Use diverse and representative training data.
• Implement mathematical processes to detect and mitigate biases.
• Develop transparent and explainable algorithms.
• Adhere to ethical standards prioritizing fairness.
• Conduct regular system audits to monitor bias continuously .

Education and Reskilling:

• Prepare the workforce for AI adoption by providing education and re-skilling opportunities.
• Enhance job roles in various industries to prevent massive job displacement .

Accountability and Liability:

• Clarify who should be held accountable for AI system decisions.
• Design AI systems that make accurate predictions without making judgment calls on behalf of humans.

Global Standards and Multi-Stakeholder Approaches:

• Develop standardized agreements that balance innovation and regulation.
• Collaborate across stakeholders to address challenges in AI development.

Emerging industry leaders in memory technologies for AI applications

When it comes to memory technologies for AI applications, our research shows that there are several companies which will stand out as global leaders in the years 2025-2026:

AMD is addressing the memory bottleneck issue with their Instinct lineup.?For instance, the?AMD Instinct MI325X?accelerator, planned for Q4 2024, features?HBM3E memory?with up to?288GB capacity?and local memory bandwidths of?6TB/second. This helps mitigate memory-related bottlenecks. Architecture Improvements: AMD’s?CDNA 4 architecture, expected in 2025, aims to deliver up to a?35x increase in AI inference performance?compared to CDNA 3. Innovations in architecture can significantly enhance AI processing efficiency.

NVIDIA is a prominent player in addressing memory bottlenecks in AI.?They offer tools like?DLProf?and?PyProf?for profiling and optimizing deep neural networks. These tools help identify bottlenecks related to CPU, GPU, and memory usage during training or inference.?Additionally, NVIDIA explores ways to overcome data loading and transfer bottlenecks when working with large datasets and multiple GPUs.

Samsung : Samsung offers a variety of memory solutions tailored for AI workloads. Their?HBM3E Shinebolt?memory, utilizing 12-layer stacking technology, provides up to?1,280GBps bandwidth?and a capacity of up to?36GB.?Compared to the previous generation, HBM3, HBM3E boasts over a?50% improvement?in performance and capacity, making it suitable for the Hyperscale AI era.

TSMC (Taiwan Semiconductor Manufacturing Company): TSMC collaborates with SK hynix to pioneer?high bandwidth memory (HBM) chips?specifically designed for AI workloads.?This partnership aims to reshape AI chip manufacturing and enhance competition in the semiconductor market .

Intel : While not exclusively focused on memory, Intel invests in research and development related to AI memory.?They are actively exploring technologies like?MRAM (Magnetoresistive Random-Access Memory), which could play a role in AI applications. Intel has been working on advanced AI chips, such as the upcoming Falcon Shores hybrid AI processor, expected to be released in late 2025. This chip is designed for high-performance computing (HPC) and AI applications. Intel has been investing heavily in its AI and datacenter business, unveiling a roadmap for next-gen Intel? Xeon products, which are critical for AI processing. Additionally, Intel’s efforts to secure advanced manufacturing technology, such as the High-NA EUV lithography tools from ASML, could enhance its production capabilities. The AI chip market is projected to grow significantly, with forecasts suggesting the market could reach $129 billion by 2025 . Intel aims to ship 100 million AI PCs by 2025, showing its ambition to capture a substantial market share.

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