Rohit Sindhu, a Senior Principal Engineer with over 22 years of experience in computer science, embedded systems, and high-performance interconnects, has made a significant mark in the storage industry with his recently granted US patent, US11237760B2: “Measuring performance metrics for data storage devices.” Sindhu’s career is defined by technical depth, innovation, and a passion for advancing the state of the art in PCIe Express, CXL, MCTP, and NVMe storage technologies. In addition to this milestone patent, Sindhu has multiple pending patents in the PCIe, CXL, and MCTP domains, further underscoring his ongoing contributions to next-generation interconnect and storage solutions. Notably, he has also contributed to the development of CXL and JEDEC specifications related to CXL and PCIe device management, reflecting his influence on industry standards and best practices. A Career Dedicated to Embedded, Memory and Storage Innovation Rohit Sindhu’s professional journey spans more than two decades, during which he has become widely recognized for his expertise in both hardware and software domains. He holds a Master’s degree in Computer Science from the University of Texas at Dallas with a Bachelor’s Degree in Computer Engineering from NIT Surat, Gujarat, India and has contributed to the development of embedded systems for mission-critical applications, architected solutions across the PCIe, MCTP and CXL protocol stacks, and driven innovation in Memory & storage performance, availability, manageability and reliability. Sindhu’s technical leadership and mentorship have impacted teams and products across the industry. He is known for bridging the gap between complex engineering challenges and practical, scalable solutions. Over the years, he has worked on a diverse range of projects, from firmware development and system-on-chip design to advanced debug systems and high-speed interconnect protocols. His work has spanned sectors including enterprise storage, data centers, defense, and emerging edge computing applications. As a Senior Principal Engineer in his current role, Sindhu continues to focus on the intersection of high-speed interconnects and data storage, developing solutions that address real-world bottlenecks and enable next-generation computing platforms. His broad technical background allows him to approach problems holistically, considering both hardware and software aspects to deliver robust, high-performance solutions. Sindhu is also an active mentor and thought leader in the technology community. He regularly shares insights on storage trends, PCIe and CXL advancements, and best practices for embedded systems design. His professional profile and further details about his career can be found on his LinkedIn page. US11237760B2: Raising the Bar for NVMe Latency Measurement Sindhu’s patent, “Measuring performance metrics for data storage devices,” addresses a long-standing challenge in the storage industry: accurately measuring the latency of NVMe commands in high-speed PCIe environments without introducing the overhead or inaccuracies common to traditional software-based tools. The Problem: Latency Measurement in Modern Storage In today’s data-driven world, the performance of NVMe-based storage systems is critical for applications ranging from cloud computing and artificial intelligence to real-time analytics and edge computing. Latency, the time it takes for a storage command to be processed and completed directly impacts application responsiveness, quality of service (QoS), and overall infrastructure efficiency. Traditional latency measurement methods, often software-based, can introduce significant observational overhead, skewing results and failing to capture the true performance characteristics of storage devices. Hardware probes, while useful, sometimes lack the granularity needed to isolate individual command latencies, especially in environments with high command throughput and parallelism. Sindhu’s Solution: An FPGA-Accelerated Approach Sindhu’s patented method leverages FPGA-based hardware logic to passively monitor PCIe transactions between the host and storage device, precisely timing the lifecycle of individual NVMe commands. The process unfolds in a series of orchestrated steps designed to ensure accuracy, scalability, and zero impact on normal system operation. Step-by-Step Breakdown: Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic. Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ. PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally. Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range. Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic. Queue Pair Creation: The host system initiates the process by creating a dedicated Submission Queue (SQ) and Completion Queue (CQ) pair for latency measurement. This isolation prevents interference from other I/O operations. The start addresses and lengths of these queues are configured in the FPGA’s test logic. Queue Pair Creation Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ. Command Injection: The host queues an NVMe command and rings the doorbell, prompting the device to fetch the command from the SQ. Command Injection PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally. PCIe Transaction Snooping: The device issues a PCIe Transaction Layer Packet (TLP) to fetch the command. The FPGA’s snooping logic monitors for read requests within the SQ address range, retrieves the transaction Tag (a unique identifier for the transaction), and records it internally. PCIe Transaction Snooping Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range. Tag Matching and Timer Start: As the FPGA continues monitoring, it matches the Tag in subsequent response packets, extracts the NVMe command ID from the TLP payload, and starts a high-resolution latency timer—typically driven by FPGA clock cycles in the nanosecond range. Tag Matching and Timer Start Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Completion Monitoring and Timer Stop: The FPGA then watches for command completion responses in the CQ address range. When the NVMe command ID in the completion response matches the saved command ID, the timer stops, and the measured latency is reported back to the host. Completion Monitoring and Timer Stop This approach ensures that performance metrics are captured with nanosecond-level accuracy and zero impact on the normal operation of the host or device, a significant advancement over existing methods. Technical Advantages and Industry Impact Sindhu’s invention offers several key advantages over traditional software and hardware-based measurement tools: Zero Observational Overhead: The FPGA operates passively on the PCIe bus, leaving host and device workloads unaffected. There is no need for intrusive software agents or kernel modifications. Zero Observational Overhead: The FPGA operates passively on the PCIe bus, leaving host and device workloads unaffected. There is no need for intrusive software agents or kernel modifications. Zero Observational Overhead: Protocol-Agnostic and Scalable: While optimized for NVMe, the method can be adapted to any PCIe-based protocol, including emerging standards like CXL.io, by reconfiguring address ranges and command parsing logic. Protocol-Agnostic and Scalable: While optimized for NVMe, the method can be adapted to any PCIe-based protocol, including emerging standards like CXL.io, by reconfiguring address ranges and command parsing logic. Protocol-Agnostic and Scalable Multi-Command Parallelism: The FPGA’s ability to track multiple Tags simultaneously enables concurrent latency measurement across many NVMe commands—critical for assessing real-world, high-throughput workloads. Multi-Command Parallelism: The FPGA’s ability to track multiple Tags simultaneously enables concurrent latency measurement across many NVMe commands—critical for assessing real-world, high-throughput workloads. Multi-Command Parallelism Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility: The solution can be implemented as standalone hardware or embedded within Smart NICs, computational storage devices, or CXL-attached memory controllers. Integration Flexibility Applications Across the Storage Ecosystem Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting. Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers. Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation. CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting. Data Center Optimization: Cloud providers and enterprise IT teams can use this technology for real-time latency analytics across large fleets of NVMe devices, enabling dynamic QoS management and proactive troubleshooting. Data Center Optimization Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers. Storage OEM Validation: Manufacturers can integrate this IP into test platforms to validate SSD latency under extreme workloads, replacing expensive and less flexible protocol analyzers. Storage OEM Validation Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation. Autonomous and Edge Systems: In latency-sensitive environments like autonomous vehicles or industrial edge computing, Sindhu’s method provides the granularity needed to certify storage subsystems for real-time operation. Autonomous and Edge Systems CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. CXL and Next-Gen Storage: As CXL adoption grows for memory pooling and computational storage, the patent’s PCIe snooping framework lays the groundwork for similar measurement techniques across new protocols. CXL and Next-Gen Storage A Visionary Leader and Mentor Beyond his technical achievements, Rohit Sindhu is recognized as a mentor and advocate for innovation in the embedded, memory, and storage communities. He has guided teams through complex engineering challenges, shared his expertise through technical talks and publications, and actively supports the next generation of engineers. Sindhu’s approach is characterized by a relentless pursuit of precision, efficiency, and scalability. His work on US11237760B2 exemplifies his commitment to solving real-world problems with elegant, practical solutions that have a lasting impact on the industry. Looking Forward As storage technologies continue to evolve to meet the demands of AI, big data, and cloud-scale infrastructure, the need for accurate and efficient performance measurement will only grow. Rohit Sindhu’s patented approach provides a robust foundation for the next wave of innovation in storage analytics and validation. With multiple pending patents in PCIe, CXL, and MCTP domains, and with his direct contributions to CXL and JEDEC specifications related to CXL and PCIe device management, Sindhu’s influence on the next generation of high-speed interconnects and storage technologies is set to expand even further. To learn more about Rohit Sindhu’s professional journey, connect with him on LinkedIn. LinkedIn LinkedIn US11237760B2 is more than a patent—it is a testament to Rohit Sindhu’s enduring impact on the technology industry, and a blueprint for the future of precise, hardware-accelerated storage performance measurement. US11237760B2 is more than a patent—it is a testament to Rohit Sindhu’s enduring impact on the technology industry, and a blueprint for the future of precise, hardware-accelerated storage performance measurement. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Echospire Media under HackerNoon’s Business Blogging Program. Learn more about the program here. here here
