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arxiv: 2606.10303 · v1 · pith:36YBRSVYnew · submitted 2026-06-09 · 💻 cs.AR · cs.DC

Isolation-aware Scheduling Framework for DNN-based End-to-End Autonomous Driving System on Tile-based Accelerators

Chenguang Zhang , Yuanpeng Zhang , Chenhao Xue , Yihan Yin , Chen Zhang , Guangyu Sun This is my paper

Pith reviewed 2026-06-27 11:47 UTC · model grok-4.3

classification 💻 cs.AR cs.DC
keywords autonomous driving systemsDNN schedulingtile-based acceleratorsend-to-end pipelinesisolationlatency constraintsprobabilistic modelingDAG scheduling
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The pith

ADS-Tile bounds reallocation of tiles among colocated DNNs to meet end-to-end deadlines with up to 32% fewer tiles.

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

The paper presents ADS-Tile, a framework that lets tile-based accelerators run dozens of heterogeneous DNNs as end-to-end autonomous driving pipelines under a 100 ms latency bound even when individual execution times vary by 3.3x. It treats tunable degree of parallelism and hardware-native tile isolation as first-class resources but explicitly accounts for the stop-migrate-restart cost of changing a task's tile count. By carving out a spatio-temporal isolation-sharing space that restricts when and where reallocation can occur, then feeding a probabilistic latency model and DAG-aware scheduler with that space, the system decides colocation and parallelism under shared deadlines. A sympathetic reader cares because the alternative is either dedicating separate hardware to each function, which is too expensive for mass-produced vehicles, or accepting unpredictable stalls that break the end-to-end chain.

Core claim

ADS-Tile combines configurable isolation and elastic reservation into a spatio-temporal isolation-sharing space that bounds where and when reallocation occurs; a probabilistic latency model and a DAG-aware runtime scheduler then use this space to decide task colocation and DoP under shared E2E deadlines. On an industry- and academia-derived ADS benchmark, ADS-Tile uses up to 32% fewer tiles than the work-conserving baseline in deadline-critical settings and cuts reallocation-induced wasted processing capacity from 17%-44% to below 1.2%.

What carries the argument

The spatio-temporal isolation-sharing space that restricts tile reallocation events among co-located DNNs along end-to-end DAGs.

If this is right

  • Reservation-based schedulers that fix DoP leave flexibility unused and require more tiles overall.
  • Work-conserving schedulers that ignore reallocation cost accumulate stalls along E2E chains and miss deadlines.
  • Controlled sharing of tiles improves both resource efficiency and latency predictability compared with either extreme.
  • The same isolation bounds allow higher task rates (10-240 Hz) without proportional growth in wasted capacity.

Where Pith is reading between the lines

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

  • The same bounding technique could be tested on other real-time multi-DNN workloads such as robotics or surveillance pipelines that also face variable execution and reallocation costs.
  • If the model holds across hardware generations, designers could reduce over-provisioning margins in future ADS chips.
  • Explicitly exposing reallocation cost as a schedulable dimension may generalize to other accelerators that support dynamic partitioning.

Load-bearing premise

The probabilistic latency model must correctly predict execution time variation and reallocation costs for the specific colocation patterns and degree-of-parallelism changes that occur in the end-to-end DAGs.

What would settle it

Run the ADS-Tile scheduler on physical tile-based hardware with the benchmark workloads and measure whether observed tile count and wasted capacity match the reported 32% savings and sub-1.2% waste; any systematic exceedance of the claimed latency bounds would falsify the result.

Figures

Figures reproduced from arXiv: 2606.10303 by Chenguang Zhang, Chenhao Xue, Chen Zhang, Guangyu Sun, Yihan Yin, Yuanpeng Zhang.

Figure 1
Figure 1. Figure 1: The workflow of a representative DNN-based ADS benchmark. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) A simplified multi-rate workflow graph, (b) patterns [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Guided Phase I for the scheduling problem. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Basic idea of two representative schedulers: Cyc. and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Guided phases II–III for the scheduling problem. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Characterization of Cyc. and Tp-driven on a tile-based ADS. Total processing power is normalized to 1, where total capacity is decomposed into idle, effective use, and waste due to reallocation (Realloc.). For cases in which some tasks time out, Miss Rate measures the percentage of timed-out tasks, and missed processing power ratio measures the ratio of dropped processing power to total processing power.(a… view at source ↗
Figure 7
Figure 7. Figure 7: Overview of the ADS-Tile framework [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Flowchart of the runtime scheduler. The ADS-Tile runtime executes the proposed scheduling algorithm across distributed tiles through a two-level scheduling architecture (Figure 9a). A host scheduler initializes partitions using the reservation table and configures data routing between sensor processing units, main memory, and partitions. Each partition then runs a local scheduler on the RISC-V core of its … view at source ↗
Figure 10
Figure 10. Figure 10: The graph abstraction and algorithms adopted for the L4 ADS benchmark. [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Ablation study. (a) Effect of dynamic reservation: Cyc. (S) vs. Cyc. (p80 projected as dashed lines). (b–c) Effect of [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The 99th-percentile E2E tail latency and deadline miss rate, with respect to the number of tiles, under different workloads [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: (a) The maximum number of cockpit chains that can be [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗
read the original abstract

Level-4+ autonomous driving systems (ADS) must run dozens of heterogeneous deep neural networks (DNNs) as end-to-end (E2E) pipelines under a strict latency constraint (<=100 ms), even as execution time varies by up to 3.3x. Cost rules out dedicating isolated hardware to each function in mass-produced ADS, so these DNNs must be densely colocated on a single chip, which introduces shared-resource contention. Tile-based accelerators expose two scheduling opportunities that conventional ADS schedulers do not exploit. First, they provide a tunable degree of parallelism (DoP): assigning more tiles raises DoP and can shorten DNN execution time. Second, they provide hardware-native isolation: tiles can be physically partitioned among co-located DNNs. But using this flexibility is expensive: changing a task's DoP triggers a stop-migrate-restart reallocation of its weights and intermediate features. At ADS task rates of 10-240 Hz, these stalls accumulate along E2E chains and threaten deadlines. Reservation-based schedulers fix DoP and leave this flexibility unused; work-conserving schedulers exploit it but assume reallocation is cheap and treat deadlines as independent. We present ADS-Tile that combines configurable isolation and elastic reservation into a spatio-temporal isolation-sharing space that bounds where and when reallocation occurs; a probabilistic latency model and a DAG-aware runtime scheduler then use this space to decide task colocation and DoP under shared E2E deadlines. On an industry- and academia- derived ADS benchmark, ADS-Tile uses up to 32% fewer tiles than the work-conserving baseline in deadline-critical settings and cuts reallocation-induced wasted processing capacity from 17%-44% to below 1.2%. Controlled spatio-temporal sharing improves resource efficiency and latency predictability for tile-based ADS.

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

2 major / 1 minor

Summary. The paper introduces ADS-Tile, an isolation-aware scheduling framework for DNN-based end-to-end autonomous driving systems on tile-based accelerators. It combines configurable isolation and elastic reservation in a spatio-temporal space, employs a probabilistic latency model to capture execution variation (up to 3.3x) and reallocation costs, and uses a DAG-aware runtime scheduler to select colocation and DoP while meeting <=100 ms E2E deadlines. On an industry- and academia-derived benchmark, it reports up to 32% fewer tiles than a work-conserving baseline and reduces reallocation-induced wasted capacity from 17-44% to below 1.2%.

Significance. If the probabilistic model is shown to be accurate for the benchmark DAG colocation patterns, the framework would demonstrate how tile-based accelerators' DoP and isolation features can be exploited for better resource efficiency in latency-critical ADS without dedicating isolated hardware per function.

major comments (2)
  1. [Abstract / Probabilistic latency model] Abstract / Probabilistic latency model section: The 32% tile reduction and <1.2% waste claims rest on the model accurately predicting execution variation and stop-migrate-restart reallocation costs (at 10-240 Hz rates) for the specific E2E DAG colocation/DoP patterns in the benchmark. No derivation, fitting procedure, or hardware validation against measured tile-based accelerator traces is supplied, which is load-bearing for confirming that decisions provably meet the latency bounds.
  2. [Empirical evaluation] Empirical results: The reported improvements lack benchmark composition details, error bars, run counts, or explicit validation that the model holds for the colocation patterns arising in the industry/academia-derived ADS DAGs; without these, the cross-baseline comparison cannot be assessed.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'industry- and academia-derived ADS benchmark' is used without naming the specific sources or pipeline composition.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the probabilistic latency model and empirical evaluation. We address each point below and will revise the manuscript accordingly to provide the requested details and transparency.

read point-by-point responses

  1. Referee: [Abstract / Probabilistic latency model] Abstract / Probabilistic latency model section: The 32% tile reduction and <1.2% waste claims rest on the model accurately predicting execution variation and stop-migrate-restart reallocation costs (at 10-240 Hz rates) for the specific E2E DAG colocation/DoP patterns in the benchmark. No derivation, fitting procedure, or hardware validation against measured tile-based accelerator traces is supplied, which is load-bearing for confirming that decisions provably meet the latency bounds.

    Authors: The probabilistic latency model is presented to capture the up to 3.3x execution variation and reallocation costs at the cited rates for the E2E DAG patterns. We agree that the manuscript would benefit from explicit inclusion of the derivation, parameter fitting procedure (based on observed accelerator behavior under varying DoP and colocation), and direct comparison of model outputs to hardware traces for the benchmark colocation patterns. In the revision we will add a dedicated subsection detailing these elements, including the probabilistic formulation and validation results, to substantiate that the scheduler decisions respect the latency bounds. revision: yes

  2. Referee: [Empirical evaluation] Empirical results: The reported improvements lack benchmark composition details, error bars, run counts, or explicit validation that the model holds for the colocation patterns arising in the industry/academia-derived ADS DAGs; without these, the cross-baseline comparison cannot be assessed.

    Authors: We acknowledge that the initial submission omitted sufficient details on the benchmark composition, statistical reporting, and model validation for the specific colocation patterns. The benchmark combines industry-derived E2E ADS pipelines with academic DAGs; we will expand the evaluation section to list the exact DNN tasks and dependencies, report error bars and the number of experimental runs, and add targeted validation experiments confirming model accuracy on the observed colocation/DoP combinations. These additions will enable direct assessment of the baseline comparisons. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The manuscript describes an empirical scheduling system (ADS-Tile) that combines isolation and elastic reservation, a probabilistic latency model, and a DAG-aware runtime scheduler. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or description. Performance numbers (32% tile reduction, waste <1.2%) are reported as benchmark outcomes rather than outputs forced by construction from inputs. The framework is therefore self-contained against external benchmarks with no load-bearing step that reduces to its own definitions or prior self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract only; no explicit free parameters, axioms, or invented entities are identifiable. The probabilistic latency model and the benchmark workloads are referenced but not detailed enough to extract fitted values or assumptions.

pith-pipeline@v0.9.1-grok · 5882 in / 1257 out tokens · 23670 ms · 2026-06-27T11:47:52.451689+00:00 · methodology

discussion (0)

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