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arxiv: 2604.11948 · v1 · submitted 2026-04-13 · 💻 cs.LG · cs.AR

Recognition: 2 theorem links

· Lean Theorem

Active Imitation Learning for Thermal- and Kernel-Aware LFM Inference on 3D S-NUCA Many-Cores

Yixian Shen , Chaoyao Shen , Jan Deen , George Floros , Andy Pimentel , Anuj Pathania
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:27 UTC · model grok-4.3

classification 💻 cs.LG cs.AR
keywords active imitation learningthermal managementscheduling policies3D S-NUCALFM inferencecore heterogeneityoracle demonstrations
0
0 comments X

The pith

Active imitation learning derives thermal-safe policies for LFM inference on 3D S-NUCA many-cores by imitating near-optimal oracle schedules.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to show that active imitation learning can create effective scheduling policies for managing thread placement and voltage scaling on 3D-stacked many-core CPUs when running large foundation model inference tasks. Traditional methods struggle with the mix of thermal limits, varying cache latencies, and different computational kernels in these models. By learning from demonstrations of optimal behavior, the approach adapts to system heterogeneity and workload specifics while keeping temperatures safe. This matters because it allows high-performance general-purpose CPUs to handle AI inference more efficiently without needing expensive GPUs, potentially lowering costs and improving accessibility for such workloads.

Core claim

AILFM is an active imitation learning based scheduling framework that learns near-optimal thermal-aware scheduling policies from oracle demonstrations. It incorporates core-level performance heterogeneity and kernel-specific behavior in large foundation models to ensure thermal safety and maximize performance with minimal runtime overhead. Experiments demonstrate that it outperforms state-of-the-art baselines and generalizes across diverse LFM workloads on 3D S-NUCA systems.

What carries the argument

Active Imitation Learning (AIL) scheduler that imitates oracle policies for thread migration and V/f scaling, tailored to core heterogeneity and LFM kernel diversity.

If this is right

  • Maintains thermal safety while maximizing performance on heterogeneous 3D S-NUCA many-cores.
  • Adapts to diverse LFM kernels without high runtime overhead.
  • Generalizes well to new LFM workloads beyond training examples.
  • Outperforms state-of-the-art baselines in experiments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Learning from oracles may replace hand-crafted models in other complex thermal management scenarios on many-cores.
  • The framework could support production deployment for varied AI inference tasks on similar hardware.
  • Similar imitation methods might address related problems like power management in heterogeneous systems.

Load-bearing premise

Oracle demonstrations of near-optimal policies exist and can be imitated with low runtime overhead while correctly capturing both core heterogeneity and kernel-specific LFM behavior to guarantee thermal safety.

What would settle it

If AILFM violates thermal limits or fails to outperform baselines when tested on a 3D S-NUCA system with a new LFM kernel not present in the oracle training data.

Figures

Figures reproduced from arXiv: 2604.11948 by Andy Pimentel, Anuj Pathania, Chaoyao Shen, George Floros, Jan Deen, Yixian Shen.

Figure 1
Figure 1. Figure 1: An abstract representation of 3D-stacked S-NUCA systems. Limited off-chip memory bandwidth and throughput are the primary bottlenecks in executing low-latency LFM inference on CPUs. LFMs are memory-intensive, high-throughput workloads requiring significant data movement between the CPU and mem￾ory during forward and backward passes. These models exhibit large memory footprints due to dynamically generated … view at source ↗
Figure 2
Figure 2. Figure 2: Processing core AMDs in 3D-stacked S-NUCA processors Furthermore, micro-architecturally identical cores in processors with 3D S-NUCA caches exhibit noticeable performance hetero￾geneity due to the non-uniformity in the LLC access latency, as depicted in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the Oracle and Learning Policies in AILFM for thermal management of 3D S-NUCA systems. The Oracle Policy 𝜋 ∗ uses MoGPR to model MPKI–IPS–thermal dynamics and label kernel-migration utilities. The Learning Policy 𝜋𝜃 applies MC Dropout for uncertainty estimation, enabling selective Oracle queries and confident autonomous actions. Router One-hot Bitmap MoGPR Source Core: Target Core [PITH_FULL_I… view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of the MoGPR-based Oracle in AILFM. The selected G P predicts migration utility from source and target core. 5.3 MoGPR-Based Oracle Demonstrations Aggressive power budgeting reduces core activity and degrades performance; migrating threads to cooler cores can sustain through￾put, but in 3D S-NUCA systems with distributed LLCs, such moves incur cold-start penalties due to lost cache locality. C… view at source ↗
Figure 5
Figure 5. Figure 5: Normalized performance of AILFM across LFMs under varying input sequence lengths, with values referenced to AILFM at 𝐿 = 128. 5.4.1 Uncertainty Estimation with MC Dropout. We employ MC Dropout [5] during inference to quantify the agent’s confidence in its decisions. We estimate predictive uncertainty by incorporating dropout layers after fully connected layers in our NN model and keeping them active during… view at source ↗
Figure 6
Figure 6. Figure 6: Normalized performance of AILFM versus baselines. frequency of 3 GHz and features a private 32 KB L1 data cache, a private 32 KB L1 instruction cache, and a shared 1 MB S-NUCA cache per core, resulting in a total 64 MB on-chip shared LLC. We use a 64 GB HBM memory with a bandwidth of 25.6 GB/s. The workloads include standalone implementations of five LFM models: BERT-base [20] on SQuAD v1.1, ViT-base [4] o… view at source ↗
Figure 7
Figure 7. Figure 7: 𝑇𝑝𝑒𝑎𝑘 comparison between baselines of 3D S-NUCA. 4 8 16 32 48 64 1 2 3 4 5 Number of Active Cores Overhead [%] [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
read the original abstract

Large Foundation Model (LFM) inference is both memory- and compute-intensive, traditionally relying on GPUs. However, the limited availability and high cost have motivated the adoption of high-performance general-purpose CPUs, especially emerging 3D-stacked Static Non-Uniform Cache Architecture (3D S-NUCA) systems. These architectures offer enhanced bandwidth and locality but suffer from severe thermal challenges and uneven cache latencies due to 3D Networks-on-Chip (NoC). Optimal management of thread migration and V/f scaling is non-trivial due to LFM kernel diversity and system heterogeneity. Existing thermal management approaches often rely on oversimplified analytical models and lack adaptability. We propose AILFM, an Active Imitation Learning (AIL)-based scheduling framework that learns near-optimal thermal-aware scheduling policies from Oracle demonstrations with minimal run-time overhead. AILFM accounts for both core-level performance heterogeneity and kernel-specific behavior in LFMs to maintain thermal safety while maximizing performance. Extensive experiments show that AILFM outperforms state-of-the-art baselines and generalizes well across diverse LFM workloads.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The manuscript proposes AILFM, an active imitation learning framework for thermal- and kernel-aware scheduling of Large Foundation Model (LFM) inference on 3D S-NUCA many-core CPUs. It learns near-optimal policies for thread migration and V/f scaling from oracle demonstrations, explicitly accounting for core-level performance heterogeneity and kernel-specific LFM behaviors to maintain thermal safety while maximizing performance. The central claim is that extensive experiments demonstrate outperformance over state-of-the-art baselines together with strong generalization across diverse LFM workloads.

Significance. If the empirical results hold, the work could be significant for enabling cost-effective LFM deployment on general-purpose 3D-stacked CPU platforms instead of GPUs. It offers a learning-based alternative to oversimplified analytical thermal models and directly addresses heterogeneity and kernel diversity, which are increasingly relevant for AI systems. The use of imitation learning to transfer near-optimal policies with low runtime overhead is a promising direction if the oracles prove robust.

major comments (3)
  1. [Abstract] Abstract: the claim that 'extensive experiments show that AILFM outperforms state-of-the-art baselines and generalizes well' is unsupported by any metrics, baseline names, workload descriptions, or methodology details. This is load-bearing for the paper's primary contribution.
  2. [Oracle demonstrations] Oracle demonstrations section: the construction, optimality verification, and fidelity of the oracle demonstrations to actual thermal dynamics and core-to-core variation are not described in sufficient detail. Because the entire AILFM policy is obtained by imitating these oracles, the absence of this information prevents verification that the learned policy improves upon or safely exceeds existing methods.
  3. [Experimental evaluation] Experimental evaluation: no quantitative results, tables, or figures reporting performance, thermal safety margins, runtime overhead, or cross-workload generalization metrics are referenced, rendering the outperformance and generalization claims unverifiable.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough and constructive review of our manuscript. We appreciate the identification of areas where additional clarity is needed and will revise the paper to strengthen the presentation of our contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'extensive experiments show that AILFM outperforms state-of-the-art baselines and generalizes well' is unsupported by any metrics, baseline names, workload descriptions, or methodology details. This is load-bearing for the paper's primary contribution.

    Authors: We agree that the abstract would be strengthened by greater specificity. In the revised version, we will expand the abstract to name the primary baselines (standard DVFS, heuristic migration, and analytical thermal-model schedulers), briefly characterize the LFM workloads (diverse inference kernels from models such as BERT and GPT variants), and report key quantitative outcomes (performance speedup, thermal safety margin, and cross-workload generalization rate) drawn from the experimental results already present in the body of the paper. revision: yes

  2. Referee: [Oracle demonstrations] Oracle demonstrations section: the construction, optimality verification, and fidelity of the oracle demonstrations to actual thermal dynamics and core-to-core variation are not described in sufficient detail. Because the entire AILFM policy is obtained by imitating these oracles, the absence of this information prevents verification that the learned policy improves upon or safely exceeds existing methods.

    Authors: We acknowledge the need for greater detail in the oracle section. We will revise this section to explicitly describe the oracle construction (offline optimization over thread-to-core mappings and V/f states using a cycle-accurate 3D thermal simulator), the verification procedure (comparison against exhaustive enumeration on reduced core counts and convergence to within a small optimality gap), and the fidelity checks (validation of simulated temperatures and per-core latency variation against hardware measurements on the target 3D S-NUCA platform). These additions will enable readers to assess the quality of the demonstrations used for imitation learning. revision: yes

  3. Referee: [Experimental evaluation] Experimental evaluation: no quantitative results, tables, or figures reporting performance, thermal safety margins, runtime overhead, or cross-workload generalization metrics are referenced, rendering the outperformance and generalization claims unverifiable.

    Authors: The manuscript already contains the requested quantitative results in dedicated tables and figures (performance and thermal comparisons, overhead measurements, and generalization analysis). However, we agree that explicit cross-references were insufficient. We will revise the experimental section to directly cite the relevant tables and figures when stating each result and will add a short summary paragraph that consolidates the key metrics for outperformance and generalization. This constitutes a partial revision focused on presentation rather than new data collection. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical imitation-learning framework with external oracle

full rationale

The paper presents AILFM as an active imitation learning scheduler that learns thermal-aware policies from oracle demonstrations on heterogeneous 3D S-NUCA systems. All central claims (outperformance, generalization, thermal safety) rest on experimental comparisons to baselines rather than any mathematical derivation, equation, or first-principles result. No self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations appear in the provided text. The oracle is treated as an external source of demonstrations; its construction is not shown to reduce to the learned policy itself. This is a standard empirical ML systems paper whose validity hinges on experimental evidence, not tautological reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the framework is described at high level without mathematical or modeling details.

pith-pipeline@v0.9.0 · 5512 in / 1071 out tokens · 69413 ms · 2026-05-10T16:27:11.059284+00:00 · methodology

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