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General Summary:
As part of Qualcomm’s Audio and Low-Power AI (LPAI) Systems group, this role focuses on power and data-path analysis, optimization, and architecture of embedded AI subsystems, with emphasis on XR and always-on use cases. The engineer will drive power-efficient system design and analysis across DSP/eNPUsubsystems by analyzing power-performance trade-offs, and enabling optimizations across memory access, data movement, and workloads for on-device AI.
Key Responsibilities
Analyze andoptimizepower consumption of LPAI subsystems (DSP,eNPU, memory hierarchy) withfocuson XR and always-on AI workloads.
Develop system-level power analysis to evaluate different audio use cases across DSPabdeNPU,
Perform detailed data-path and memory-access analysis (TCM, LLC, DDR) toidentifybottlenecksimpactingpower efficiency.
Drive power optimization techniquesincludingclock/BW voting, workload partitioning, scheduling, and data reuse strategies.
Collaborate with HW, SW, andPdMteams to revieweNPUpower architecture and low-power feature roadmap.
Execute lab-based power measurements, correlate silicon data with modelling, and propose optimization strategies.
Support system integration, benchmarking, and commercialization of power-optimized LPAI solutions across Mobile, XR, Compute, and IoT platforms.
Document power analysis methodologies, findings, and architectural recommendations for internal stakeholders.
Requirements
Strong fundamentals in power modeling, power analysis, and system-level power optimization.
Experience with embedded processor architectures such as DSPs and NPUs, with understanding ofeNPUpower behavior.
Hands-on experience with power measurement setups such as Kratos, tools, and data analysis techniques
Strong programming skills in Python for analysis, modeling, and automation.
Solid understanding of memory systems, data movement, bandwidth analysis, andCache memorystrategies.
Experience working with embedded platforms, RTOS, and performance/power profiling tools.
Knowledge of fixed-point implementation and low-power optimization techniques.
Ability to work across cross‑functional and geographically distributed teams.
Preferred Qualifications
Experience with Qualcomm DSP and LPAI architectures, SDKs, or internal power tools.
Backgroundin audio, oralways-onAI use cases.
Exposure to ML inference workloads and their power-performance characteristics
Educational Qualifications
Bachelor’s/Master’s/PhD degree in Electrical Engineering, Electronics and Communication, Computer Science, or related field.
Minimum Qualifications:
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