Leading the architecture definition for a high performance GPU block at NVIDIA enabled a 15 percent reduction in power consumption while maintaining peak clock frequency targets under tight thermal constraints. This initiative required coordinating cross functional teams to resolve complex timing closure issues during the late stages of the tape out process. Applying these high stakes design optimization skills to the innovative AI compute architectures developed at d-Matrix presents a compelling technical challenge. My expertise in RTL design and architectural trade offs directly aligns with the mission to build high efficiency inference engines for next generation data centers.
During my tenure at Intel, I transitioned legacy blocks to advanced nodes by utilizing SystemVerilog and SystemC for rigorous cycle accurate modeling, which improved simulation throughput by 30 percent. While at AMD, I spearheaded the FPGA prototyping phase for a new memory controller, catching three critical functional bugs that would have delayed silicon delivery. My work at Qualcomm involved deploying formal verification techniques to ensure zero defect compliance for low power sleep modes, effectively saving thousands of verification man hours. These methods ensure robust silicon quality, a priority that d-Matrix requires for its cutting edge machine learning hardware.
d-Matrix is pioneering the digital in memory compute landscape, a field that demands extreme precision in power delivery and low latency architectural design. My background in computer architecture, honed during my time at NVIDIA, provides the technical depth needed to bridge the gap between abstract algorithmic requirements and physical silicon constraints. By combining deep RTL design proficiency with my focus on low power methodologies, I contribute directly to optimizing your chip density goals. Scaling your compute tiles while maintaining strict power budgets is where my specific expertise in high performance hardware systems adds immediate value.
Aligning my experience with the ambitious scaling roadmap at d-Matrix will foster faster iteration cycles and more reliable product releases for your flagship AI solutions. I welcome a discussion regarding the specific technical hurdles your silicon team currently faces and how my background in verified, high performance design can assist in meeting your aggressive performance targets. My technical understanding of complex compute pipelines ensures I contribute to your objectives immediately upon joining the engineering group. Please contact me to schedule a technical review of how I can support your silicon engineering group.
Best regards,
Michael Harris
