Throughput-Optimized Processor Microarchitecture: Coordinating Core, NoC, and Memory Subsystems
Bibliographic record
Abstract
The exponential growth of data-intensive workloads, from deep learning inference to high-performance computing (HPC) simulations, has driven a paradigm shift in processor design-prioritizing throughput over single-threaded latency. However, maximizing system throughput requires more than just increasing computational density; it demands seamless coordination between three critical subsystems: processing cores, Network-on-Chip (NoC) interconnects, and memory hierarchies. This paper presents a comprehensive analysis of throughput-optimized microarchitecture design, focusing on the interdependencies and coordination mechanisms that eliminate bottlenecks across these subsystems. We first examine the architectural principles guiding each component's design for throughput, including parallel core arrays, low-latency NoC topologies, and memory-centric optimizations like Processing-in-Memory (PIM). Through a detailed exploration of coordination strategies-such as static scheduling, resource partitioning, and cross-subsystem awareness-we demonstrate how unifying these subsystems can mitigate data movement overheads, the primary limiter of modern processor efficiency. Case studies of state-of-the-art architectures (e.g., Groq Tensor Streaming Processor, TOP-PIM) validate the impact of coordinated design, showing up to 85% reduction in Energy-Delay Product (EDP) and 4x throughput improvement for parallel workloads compared to disjointed designs. Finally, we outline future research directions, including heterogeneous subsystem integration and AI-driven dynamic coordination, to address emerging challenges in extreme-scale computing. This work underscores that true throughput optimization is a system-level problem, requiring holistic design across cores, NoCs, and memory to unlock the full potential of next-generation processors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".