Unlocking the Future: Next-Gen At-Memory AI Inference Accelerator

Unlocking the Future: Next-Gen At-Memory AI Inference Accelerator

Table of Contents

1. Introduction
2. The Evolution of AI and Neural Networks
3. Challenges in AI Inference
4. The Importance of Accuracy in AI Workloads
5. Architecture for Efficient AI Inference
6. Introducing Boca Ria: The Next Generation AI Inference Device
7. The Innovations of Boca Ria
8. The Power of Memory Compute
9. Scalability and Flexibility of Boca Ria
10. The Software Ecosystem: Imagine SDK
11. Performance and Energy Efficiency of Boca Ria
12. Conclusion

Introduction

Welcome to Hot Chips! In this article, we will be diving into the world of AI acceleration and exploring the latest innovations in AI inference. Over the past decade, the field of artificial intelligence has experienced exponential growth, thanks to advancements in neural network architectures. At Tether Ai, we have been at the forefront of this revolution, striving to provide efficient and high-performance compute solutions for today's AI workloads.

The Evolution of AI and Neural Networks

It all started with the release of the AlexNet paper, which marked the beginning of the AI summer. Since then, we have witnessed an explosion of neural network architectures. Tether Ai was founded in 2018 with the goal of developing a new method of compute to meet the increasing computational requirements and power efficiency needed for AI workloads.

Challenges in AI Inference

AI inference presents unique challenges for chip developers. The computational requirements have significantly increased, and power efficiency has become a crucial factor. Additionally, the ever-changing landscape of neural networks requires flexibility in architecture design. Lastly, in the case of inference, accuracy is of utmost importance. Inefficient inference can lead to costly mistakes, both in terms of financial losses and potential harm to human lives.

The Importance of Accuracy in AI Workloads

Accuracy plays a vital role in AI workloads, particularly in recommendation engines and autonomous vehicles. In fact, 35% of Amazon's revenue is attributed to their recommendation system. Furthermore, in applications like autonomous vehicles, accuracy directly impacts safety. Ensuring the most accurate results while maintaining energy efficiency is a balancing act that we strive to achieve with our architecture.

Architecture for Efficient AI Inference

To address the challenges Mentioned earlier, we have developed an architecture that combines a coarse-grained structure for optimal compute performance with the right data types to ensure accuracy and energy efficiency. Our research has led us to two key data types: bf16, which offers the same level of accuracy as fp32 but with better efficiency, and our new fp8 data type, which quadruples the efficiency of bf16 while preserving accuracy.

Introducing Boca Ria: The Next Generation AI Inference Device

Today, at Hot Chips, we are thrilled to introduce our next-generation AI inference device, Boca Ria. Boca Ria offers extraordinary performance, with two petaflops of inference acceleration and an impressive 30 tops per watt energy efficiency. This breakthrough is made possible by our innovative at-memory compute architecture, where the compute element is directly attached to memory cells.

The Innovations of Boca Ria

Boca Ria's architecture revolutionizes data movement, which has been a major energy bottleneck in neural network compute. By placing the compute element close to the data, we minimize energy consumption, maximizing efficiency. We have also designed the architecture to Scale with the granularity necessary for accelerating neural networks, ensuring compatibility with current and future architectures.

The Power of Memory Compute

Memory compute is at the heart of Boca Ria's architecture. Traditional architectures heavily rely on moving data from external memory or caches, resulting in energy inefficiency. In contrast, our at-memory compute structure eliminates unnecessary data movement, significantly reducing energy consumption. By leveraging standard digital logic processes and SRAM cells, we achieve remarkable energy efficiency while maintaining high performance.

Scalability and Flexibility of Boca Ria

Boca Ria's scalability and flexibility make it an ideal solution for various applications. Our architecture allows us to tailor the number of memory banks to fit different form factors and power envelopes. From sub-1-watt devices to high-performance server implementations, Boca Ria's scalability ensures that we can address a wide range of price-performance points. Additionally, our chip-to-chip interconnect capability, using PCI Express, enables the expansion of Boca Ria to power the largest natural language processing networks.

The Software Ecosystem: Imagine SDK

A powerful hardware solution is only as good as its software support. That's why we have developed the Imagine Software Development Kit (SDK), which simplifies the process of converting neural networks from popular machine learning frameworks like TensorFlow and PyTorch into efficient kernel code for Boca Ria's RISC-V processors. The Imagine SDK includes a model garden with pre-created neural networks, automated quantization capabilities, compilation and mapping tools, and a user-friendly runtime with convenient APIs.

Performance and Energy Efficiency of Boca Ria

Thanks to its energy-efficient design and innovative architecture, Boca Ria achieves exceptional performance and energy efficiency. When compared to existing solutions, Boca Ria provides up to 80 times more throughput for vision networks and eight times greater throughput for natural language processing networks like BERT Base. In terms of energy efficiency, Boca Ria outperforms with 15 times greater queries per Second per watt. These noteworthy improvements unlock new possibilities for AI workloads while reducing power consumption.

Conclusion

In conclusion, Boca Ria represents a significant milestone in AI inference acceleration. Its at-memory compute architecture, scalability, and flexible software ecosystem make it a Game-changer in the field of AI. By addressing the challenges in computational requirements, power efficiency, and accuracy, Boca Ria sets a new standard for performance, energy efficiency, and scalability. We are excited to continue pushing the boundaries of AI acceleration and empowering the development of groundbreaking applications.

Highlights

• Boca Ria: The next-gen AI inference device
• Innovative at-memory compute architecture
• Scalability and flexibility for various applications
• Imagine SDK: Simplifying software development for Boca Ria
• Unparalleled performance and energy efficiency

FAQ

Q: What is the main advantage of Boca Ria over traditional architectures? A: Boca Ria's at-memory compute architecture drastically reduces energy consumption by minimizing data movement, resulting in higher energy efficiency and performance.

Q: Can Boca Ria support different neural network architectures? A: Yes, Boca Ria is designed to be compatible with various neural network architectures. Its fine-tuned granularity and flexible data types enable efficient acceleration of both current and future networks.

Q: How does Boca Ria compare to existing solutions in terms of performance? A: Boca Ria outperforms existing solutions, providing up to 80 times more throughput for vision networks and eight times greater throughput for natural language processing networks like BERT Base.

Q: Is Boca Ria scalable? A: Absolutely. Boca Ria's architecture allows for scalability, making it suitable for a wide range of applications. From low-power devices to high-performance server implementations, Boca Ria can adapt to different form factors and power envelopes.

Q: What software support does Boca Ria offer? A: Boca Ria is accompanied by the Imagine Software Development Kit (SDK), which simplifies the development process by converting neural networks from popular machine learning frameworks into efficient kernel code for Boca Ria's RISC-V processors.

Resources:

• Tether Ai
• Hot Chips