Cutting-Edge AI Hardware Showcase: Cerebras, Mythic, Tesla, and More!

Cutting-Edge AI Hardware Showcase: Cerebras, Mythic, Tesla, and More!

Table of Contents:

1. Introduction
2. Cerebrus's Wafer Scale Engine
3. Mythic's Analog Compute
4. Tesla Dojo
5. Untether AI's Data Center Chip
6. Tachyum Prodigy
7. XMOS for Edge AI
8. Conclusion

Article

1. Introduction Welcome to the AI Hardware Show! In this episode, we will be discussing some exciting advancements in AI hardware, including wafer scale chips and edge tips. We will explore the capabilities, applications, and potential of these technologies. Stay tuned for more information!

2. Cerebrus's Wafer Scale Engine One of the groundbreaking advancements in AI hardware is Cerebrus's wafer scale engine. This machine learning processor is the size of a single wafer, with hundreds of thousands of cores and gigabytes of local memory. It offers a unique position in the industry, providing low latency and high performance compared to multiple small chips. Cerebrus has secured over 700 million dollars in funding, and its third-generation wave scale engine is already in development.

3. Mythic's Analog Compute Analog compute, also known as computing memory, is another significant development in AI hardware. Mythic, a company that has been around for a decade, works on analog compute using memory to perform computations at low power. With Flash transistors acting as resistors, Mythic's analog compute can store weights of a neural network in its memory cells. This approach enables efficient matrix multiplication and reduces reliance on external memory.

4. Tesla Dojo Tesla's Dojo is a chip designed to replace GPUs in data centers and scale up AI workloads. While its primary use is for video labeling in self-driving AI, Dojo is a multi-generational product with broad applications. Tesla aims to accelerate data labeling, reduce power consumption, and gain an advantage in the AI market. The Dojo supercomputer, equipped with 25 chips per wafer, is expected to be fully deployed by the end of next year.

5. Untether AI's Data Center Chip Untether AI's data center chip is a high-performance, high-efficiency AI accelerator with 1400 RISC-V cores on a single piece of silicon. This chip offers impressive performance, delivering 2 petaflops of FBA performance at peak power consumption. Untether AI combines the power efficiency of in-memory computation with the robustness of digital processing, resulting in a groundbreaking chip architecture for neural net inference.

6. Tachyum Prodigy Tachyum's Prodigy chip promises an architecture that combines high performance with energy efficiency. With 128 cores and AV2 1024-bit Vector units, Prodigy aims to deliver 5.7 GHz frequency, making it suitable for HPC and AI workloads. The chip's memory bandwidth of almost one terabyte per second ensures efficient processing of AI workloads facing memory limitations.

7. XMOS for Edge AI XMOS is a British company specializing in edge AI solutions. Its x-cores architecture offers software-defined DSP and I/O capabilities, making it flexible and economical. The architecture allows designers to allocate cores to different functions, including AI acceleration. XMOS' crossover processors combine the flexibility of application processors with the low power and real-time operation of microcontrollers, making them ideal for voice applications and AI tasks.

8. Conclusion In conclusion, the AI hardware industry is witnessing exciting developments in wafer scale chips, analog compute, edge AI, and more. Companies like Cerebrus, Mythic, Tesla, Untether AI, Tachyum, and XMOS are pushing the boundaries of AI hardware performance, efficiency, and versatility. These advancements will have a significant impact on various industries, enabling faster, more efficient, and more accurate AI applications. As the field progresses, we can expect even more innovations and advancements in AI hardware. Stay tuned for further updates and discoveries in this fascinating field.

FAQ

Q: What is wafer scale engine? A: A wafer scale engine is a machine learning processor the size of a single wafer, with hundreds of thousands of cores and gigabytes of local memory.

Q: What is analog compute? A: Analog compute, also known as computing memory, is a technology that uses memory to perform computations at low power, particularly matrix multiplication operations.

Q: What is Tesla Dojo? A: Tesla Dojo is a chip designed to replace GPUs in data centers for AI workloads, with a focus on video labeling in self-driving AI.

Q: What is untether AI's data center chip? A: Untether AI's data center chip is a high-performance, high-efficiency AI accelerator with 1400 RISC-V cores on a single piece of silicon.

Q: What is Tachyum Prodigy? A: Tachyum Prodigy is a chip that aims to provide high performance and energy efficiency, with 128 cores and AV2 1024-bit Vector units.

Q: What is XMOS? A: XMOS is a British company specializing in edge AI solutions, with a flexible architecture that combines software-defined DSP and I/O capabilities.