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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".