Top-20 AI accelerators

The landscape of AI accelerators is highly dynamic, with many companies and research institutions developing specialized hardware to speed up artificial intelligence and machine learning tasks. These accelerators are designed to handle the massive computational requirements of training and running AI models more efficiently than general-purpose CPUs or GPUs. Below is a list of notable AI accelerators, which includes both commercially available products and significant research projects. Keep in mind that the field is rapidly evolving, and new entrants may have emerged since this information was compiled.

Commercially Available AI Accelerators

1. NVIDIA GPUs – NVIDIA’s range, especially the Tesla, Quadro, and the more recent A100 and H100 GPUs, are widely used in AI research and production for their powerful CUDA cores and Tensor Cores optimized for deep learning.

2. Google TPU (Tensor Processing Unit) – Google’s TPU is specifically designed for TensorFlow, an open-source machine learning framework. TPUs are available through Google Cloud.

3. Intel Nervana NNP (Neural Network Processors) – Intel has developed AI accelerators like the Nervana NNP and is also enhancing its Xeon processors with AI capabilities.

4. AMD Instinct – AMD’s Instinct accelerators are designed for high-performance computing (HPC) and AI workloads, competing directly with NVIDIA’s offerings.

5. Graphcore IPU (Intelligence Processing Unit) – The Graphcore IPU is designed from the ground up for machine intelligence workloads and aims to deliver high performance for both training and inference tasks.

6. Cerebras Wafer-Scale Engine – Cerebras’ approach is unique, creating a massive wafer-scale engine specifically designed for AI workloads, boasting an unprecedented number of transistors.

7. Habana Labs Gaudi AI Processor – Acquired by Intel, Habana Labs offers the Gaudi processor for training and Goya for inference, focusing on efficiency and performance.

8. Cambricon Technologies – A Chinese company that provides AI chips for cloud computing and edge devices, targeting both training and inference.

9. Tachyum Prodigy – Tachyum claims its Prodigy processor is the world’s first “universal processor,” offering high performance for AI, HPC, and general-purpose computing.

10. AWS Inferentia – Amazon Web Services offers Inferentia, a custom chip designed to deliver high-throughput, low-latency inference performance at a lower cost.

Research and Emerging Technologies

11. MIT’s Eyeriss – A research project focusing on energy-efficient AI chip designs for deep neural network computations.

12. Stanford’s DAWNbench – A benchmark suite for end-to-end deep learning training and inference, which includes discussions on efficient hardware.

13. RISC-V based AI Accelerators – Various projects and startups are exploring the use of the open-source RISC-V architecture for creating customizable and efficient AI accelerators.

14. SambaNova Systems – Founded by researchers from Stanford University, SambaNova offers DataScale, an integrated software and hardware system for AI applications.

15. Groq – A startup by former Google engineers, offering an innovative tensor streaming processor architecture aimed at AI and machine learning workloads.

16. Mythic AI – Mythic focuses on producing low-power AI accelerators for edge devices, using analog computation for efficiency.

17. Lightmatter – Working on photonic AI accelerators, which use light instead of electricity for computations, promising significant improvements in speed and efficiency.

18. SiFive Intelligence – SiFive is developing RISC-V based cores and processors that are optimized for AI and machine learning tasks.

19. Tenstorrent – A company developing scalable and programmable processors for AI applications, founded by industry veterans.

20. Fujitsu’s DLU (Deep Learning Unit) – Part of their broader computing solutions, Fujitsu’s DLU is aimed at accelerating deep learning workloads.

This list represents a snapshot of a rapidly advancing field. Each of these accelerators has its strengths and is targeted at different segments of the AI workload market, from cloud data centers to edge devices. As AI technologies continue to evolve, we can expect to see further innovations and new entries in this space.