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"Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling," a Presentation from Mythic

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Edge AI and Vision Alliance
Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/mythic/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Mike Henry, CEO and Founder of Mythic, presents the "Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling" tutorial at the May 2019 Embedded Vision Summit. AI inference at the edge will continue to create insatiable demand for compute performance in power- and cost-constrained form factors. Taking into account past trends, continuous scale-up of algorithms and the real economic value now being generated by AI at the edge, a demand for 1000x more compute over the next 10 years is not out of the question. Mythic is a pioneer in analog compute, a key technology that will take us well beyond the end of Moore’s Law and deliver powerful, easy to use compute at the edge to meet this demand. In this presentation, Henry discusses Mythic’s unique IPU architecture that combines analog compute with compute-in-memory, delivering an unparalleled combination of AI inference performance and energy efficiency. He also highlights the advantages of using the Mythic architecture in edge applications such as DNN-enabled video surveillance cameras.

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© 2019 Mythic, Inc. Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling Mike Henry – CEO/Founder Mythic May 2019
© 2019 Mythic, Inc. The Trends are Clear: • Insatiable demand for DNN compute at the edge will only increase • Models like ResNet-50 are considered “toys” in production systems • Developers need Bigger models that handle more edge cases • Applications run many concurrent models with tough latency requirements 2 The Largest Problems you See Should be the Smallest Problem you Think About
© 2019 Mythic, Inc. Compute Needs to Keep Up Faster training and AI-focused compute will drive further scientific breakthroughs Inference hardware will allow rapid scale- up of powerful algorithms Semiconductor TAM for AI ($B) – Barclay’s Research 0 5 10 15 20 25 30 2016 2017 2018 2019 2020 2021 2022 2023 Training Data center inference Edge inference
© 2019 Mythic, Inc. Mythic
© 2019 Mythic, Inc. Mythic Focused on Scaling Inference Training Inference • Cost • Power • Easy Software for Deployment • Maximize Compute and Memory • Keep the Two Close • Few Other Limits
© 2019 Mythic, Inc. Decreasing Cost, Exponentially Increasing Market Size Defines Semiconductors
© 2019 Mythic, Inc. Moore’s Law is Over GlobalFoundries Stops All 7nm DevelopmentMoore’s Law Comes Up Short
© 2019 Mythic, Inc. AI compute Needs Something that Resets the Clock Just like these did to their predecessors NAND Flash RF CMOS CMOS Imaging
© 2019 Mythic, Inc. Targeting High-Value Inference Applications Cloud edge and datacenter On premise aggregation High value edge ▪ AI applications shifting in large scale to DNNs ▪ Offers compute density unlike anything on the market ▪ Targets for Mythic – On-premise aggregation – Cloud edge/ datacenter – High-value edge ▪ Roadmap envisions mobile, consumer versions
© 2019 Mythic, Inc. Mythic IPU – Analog DNN Inference Co-processor Host PCIe ▪ Co-processor on the PCIe bus ▪ Weights stored in on-chip flash (INT8) ▪ Compute with analog technology ▪ 19x19mm BGA package ▪ Multiple chips can be tiled on a single PCIe card or in a system
© 2019 Mythic, Inc. Mythic Enables High Compute in Small Form-Factors 11
© 2019 Mythic, Inc. Mythic Features In-Memory DNN Compute In-Memory DNN Tile Array of Tiles Matrix Multiplication Accelerator (MMA)
© 2019 Mythic, Inc. 13 Neural networks are largely matrix multiplies Primary DNN Calculation is Input Vector * Weight Matrix = Output Vector Input Data Neuron Weights Outputs Equations 𝑋0 𝑋1 ⋯ 𝑋 𝑁 ∗ 𝐴0 𝐵0 𝐶0 𝐴1 𝐵1 𝐶1 ⋯ ⋯ ⋯ 𝐴 𝑁 𝐵 𝑁 𝐶 𝑁 = 𝑌𝐴 = 𝑋0 𝐴0 + 𝑋1 𝐴1 + 𝑋2 𝐴2 𝑌𝐵 = 𝑋0 𝐵0 + 𝑋1 𝐵1 + 𝑋2 𝐵2 𝑌𝐶 = 𝑋0 𝐶0 + 𝑋1 𝐶1 + 𝑋2 𝐶2 𝑇 In-Memory Compute
© 2019 Mythic, Inc. 14 Analog circuits give us the MAC we need
© 2019 Mythic, Inc. Single Chip, Multiple Apps (e.g. 1080P30 SSD & Classify) PCIe Switch Multiple Chips, Cascaded via PCIe (e.g. 4K30 SSD & Classify) Flexible, Scalable Architecture
© 2019 Mythic, Inc. Low Power DNN Inference Solution 16 Nvidia Jetson TX2 Qualcomm835 Nvidia Tesla T4 Habana Goya HL-100 Mythic IPU Frames Per Second/Watt Inference Capability per Watt for Common DNN Processors ResNet-50, INT8, and Batch=1
© 2019 Mythic, Inc. Training (In the cloud) Optimize & Compile TF and ONNX Deploy (On Mythic’s Compiler) Performance Estimate Deploy to Silicon • Firmware • Linux and Android Drivers • Quantization + Compression • Mapping to processing and storage tiles • Machine code generation Automatic: (On the system) Develop with Latest Networks and Frameworks
© 2019 Mythic, Inc. Simple Run-Time API to Load DNN & Run Inference Host Processor PCIe
© 2019 Mythic, Inc. Mythic Solutions for Security and Surveillance Single chip in camera On premise NVR SoC Camera+Lidar Lidar Power 1-2W 4-32W 4-32W Use cases Low bandwidth to host (e.g. radio link, remote install) Large number of cameras Heavy processing (e.g. video) High risk pedestrian, object detection SoC SoC
© 2019 Mythic, Inc. Mythic’s analog compute lets us deliver what no else can • Large, powerful models entirely on chip and instantly computable • Unmatched performance and power efficiency for a given solution cost • Rich product roadmap that will far surpass all-digital approaches as Moore’s Law and DRAM bottlenecks tighten • Powerful SDK that makes quantization and compilation easy 20 Video Redaction & PrivacyObject Tracking & Identification Image Enhancement and Compression
© 2019 Mythic, Inc. Conclusions • Insatiable demand for DNN edge compute will only grow: • Bigger models • Huge memory requirements • Ultra-low latency • Mythic’s analog compute is the offramp from the End of Moore’s Law • Keep a close eye on us this year! 21
© 2019 Mythic, Inc. Resources • info@mythic-ai.com • www.mythic-ai.com Mythic @ Hot Chips 2018 https://medium.com/mythic-ai/mythic-hot-chips-2018-637dfb9e38b7 A Peek Into Software Engineering at Mythic https://medium.com/mythic-ai/a-peek-into-software-engineering-at-mythic- 1b0ca5522868 22

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"Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling," a Presentation from Mythic

  • 1. © 2019 Mythic, Inc. Pioneering Analog Compute for Edge AI to Overcome the End of Digital Scaling Mike Henry – CEO/Founder Mythic May 2019
  • 2. © 2019 Mythic, Inc. The Trends are Clear: • Insatiable demand for DNN compute at the edge will only increase • Models like ResNet-50 are considered “toys” in production systems • Developers need Bigger models that handle more edge cases • Applications run many concurrent models with tough latency requirements 2 The Largest Problems you See Should be the Smallest Problem you Think About
  • 3. © 2019 Mythic, Inc. Compute Needs to Keep Up Faster training and AI-focused compute will drive further scientific breakthroughs Inference hardware will allow rapid scale- up of powerful algorithms Semiconductor TAM for AI ($B) – Barclay’s Research 0 5 10 15 20 25 30 2016 2017 2018 2019 2020 2021 2022 2023 Training Data center inference Edge inference
  • 4. © 2019 Mythic, Inc. Mythic
  • 5. © 2019 Mythic, Inc. Mythic Focused on Scaling Inference Training Inference • Cost • Power • Easy Software for Deployment • Maximize Compute and Memory • Keep the Two Close • Few Other Limits
  • 6. © 2019 Mythic, Inc. Decreasing Cost, Exponentially Increasing Market Size Defines Semiconductors
  • 7. © 2019 Mythic, Inc. Moore’s Law is Over GlobalFoundries Stops All 7nm DevelopmentMoore’s Law Comes Up Short
  • 8. © 2019 Mythic, Inc. AI compute Needs Something that Resets the Clock Just like these did to their predecessors NAND Flash RF CMOS CMOS Imaging
  • 9. © 2019 Mythic, Inc. Targeting High-Value Inference Applications Cloud edge and datacenter On premise aggregation High value edge ▪ AI applications shifting in large scale to DNNs ▪ Offers compute density unlike anything on the market ▪ Targets for Mythic – On-premise aggregation – Cloud edge/ datacenter – High-value edge ▪ Roadmap envisions mobile, consumer versions
  • 10. © 2019 Mythic, Inc. Mythic IPU – Analog DNN Inference Co-processor Host PCIe ▪ Co-processor on the PCIe bus ▪ Weights stored in on-chip flash (INT8) ▪ Compute with analog technology ▪ 19x19mm BGA package ▪ Multiple chips can be tiled on a single PCIe card or in a system
  • 11. © 2019 Mythic, Inc. Mythic Enables High Compute in Small Form-Factors 11
  • 12. © 2019 Mythic, Inc. Mythic Features In-Memory DNN Compute In-Memory DNN Tile Array of Tiles Matrix Multiplication Accelerator (MMA)
  • 13. © 2019 Mythic, Inc. 13 Neural networks are largely matrix multiplies Primary DNN Calculation is Input Vector * Weight Matrix = Output Vector Input Data Neuron Weights Outputs Equations 𝑋0 𝑋1 ⋯ 𝑋 𝑁 ∗ 𝐴0 𝐵0 𝐶0 𝐴1 𝐵1 𝐶1 ⋯ ⋯ ⋯ 𝐴 𝑁 𝐵 𝑁 𝐶 𝑁 = 𝑌𝐴 = 𝑋0 𝐴0 + 𝑋1 𝐴1 + 𝑋2 𝐴2 𝑌𝐵 = 𝑋0 𝐵0 + 𝑋1 𝐵1 + 𝑋2 𝐵2 𝑌𝐶 = 𝑋0 𝐶0 + 𝑋1 𝐶1 + 𝑋2 𝐶2 𝑇 In-Memory Compute
  • 14. © 2019 Mythic, Inc. 14 Analog circuits give us the MAC we need
  • 15. © 2019 Mythic, Inc. Single Chip, Multiple Apps (e.g. 1080P30 SSD & Classify) PCIe Switch Multiple Chips, Cascaded via PCIe (e.g. 4K30 SSD & Classify) Flexible, Scalable Architecture
  • 16. © 2019 Mythic, Inc. Low Power DNN Inference Solution 16 Nvidia Jetson TX2 Qualcomm835 Nvidia Tesla T4 Habana Goya HL-100 Mythic IPU Frames Per Second/Watt Inference Capability per Watt for Common DNN Processors ResNet-50, INT8, and Batch=1
  • 17. © 2019 Mythic, Inc. Training (In the cloud) Optimize & Compile TF and ONNX Deploy (On Mythic’s Compiler) Performance Estimate Deploy to Silicon • Firmware • Linux and Android Drivers • Quantization + Compression • Mapping to processing and storage tiles • Machine code generation Automatic: (On the system) Develop with Latest Networks and Frameworks
  • 18. © 2019 Mythic, Inc. Simple Run-Time API to Load DNN & Run Inference Host Processor PCIe
  • 19. © 2019 Mythic, Inc. Mythic Solutions for Security and Surveillance Single chip in camera On premise NVR SoC Camera+Lidar Lidar Power 1-2W 4-32W 4-32W Use cases Low bandwidth to host (e.g. radio link, remote install) Large number of cameras Heavy processing (e.g. video) High risk pedestrian, object detection SoC SoC
  • 20. © 2019 Mythic, Inc. Mythic’s analog compute lets us deliver what no else can • Large, powerful models entirely on chip and instantly computable • Unmatched performance and power efficiency for a given solution cost • Rich product roadmap that will far surpass all-digital approaches as Moore’s Law and DRAM bottlenecks tighten • Powerful SDK that makes quantization and compilation easy 20 Video Redaction & PrivacyObject Tracking & Identification Image Enhancement and Compression
  • 21. © 2019 Mythic, Inc. Conclusions • Insatiable demand for DNN edge compute will only grow: • Bigger models • Huge memory requirements • Ultra-low latency • Mythic’s analog compute is the offramp from the End of Moore’s Law • Keep a close eye on us this year! 21
  • 22. © 2019 Mythic, Inc. Resources • info@mythic-ai.com • www.mythic-ai.com Mythic @ Hot Chips 2018 https://medium.com/mythic-ai/mythic-hot-chips-2018-637dfb9e38b7 A Peek Into Software Engineering at Mythic https://medium.com/mythic-ai/a-peek-into-software-engineering-at-mythic- 1b0ca5522868 22