Joint Scheduling of Multi-Band Radar Sensing and DNN Inference for Cross-Stage Parallelism

Abstract: This paper studies end-to-end latency minimization for a multi-band radar sensing and deep neural network (DNN) inference pipeline. Unlike conventional stage-wise designs that treat radar sensing and DNN inference as two sequential stages, the proposed framework exploits cross-stage parallelism by allowing the inference branch associated with a sensed band to start as soon as that band completes sensing, without waiting for all bands to finish. To characterize this interaction, we formulate a joint scheduling problem that couples sensing-time allocation, branch release timing, and non-preemptive multi-core execution of a directed acyclic graph (DAG) under sensing-feasibility, precedence, and core-capacity constraints. Since the resulting problem is combinatorial and strongly time-coupled, we further develop a release-aware heuristic that evaluates each sensing decision according to its downstream impact on the DAG makespan, together with a greedy list scheduler for multi-core DAG execution under release times. Simulation results show that the proposed design can effectively exploit cross-stage parallelism and reduce end-to-end latency relative to a decoupled baseline in many heterogeneous sensing scenarios, while also clarifying the operating regimes in which the latency gain becomes limited.

• The paper develops a heuristic approach, combining release-aware DAG scheduling and one-step rollout sensing, mitigating delays and outlining when cross-stage parallelism improves timing.
• Joint scheduling, which enables start of DNN branch execution upon each radar band completion, achieves latency reductions exceeding 18% in initially sensing-constrained, heterogeneous setups.
• The results layout system performance varying from masterly alignment in sensing-constrained scenarios to potential regressions as core count increases or band heterogeneity decreases, which drives the need for more robust scheduling methodologies.

Motivation and problem setting

This paper addresses a coupling that is largely absent from both the radar-sensing and accelerator-scheduling literatures: when a multi-band radar feeds a multi-branch DNN, the completion time of each band's sensing determines the release time of that band's inference branch, so end-to-end latency depends jointly on band activation order and DAG execution on the accelerator (2604.18520). Conventional designs treat sensing and inference as strictly sequential stages: all bands must finish before inference begins, forfeiting any opportunity to overlap branch computation with ongoing sensing. The authors formulate a joint scheduling problem over (i) time-division band activation under time-varying SINR feasibility, (ii) non-preemptive job-to-core assignment on a $C$-core accelerator, and (iii) start/finish times subject to DAG precedence, with the objective of minimizing the makespan $\max_v f_v$.

The system model captures two effects often abstracted away elsewhere. First, sensing follows an information-accumulation model where each activated slot adds $g_k(\gamma_k(t)) = B_k\log(1+\gamma_k(t))$ units toward a per-band threshold $\eta_k$, and only bands whose instantaneous SINR exceeds a threshold are eligible. Second, job latency is mapping-dependent: on-chip data forwarding between dependent jobs is possible only if they share a core and branch; otherwise outputs spill to off-chip memory at substantially higher read/write cost. This creates an explicit locality-versus-concurrency tradeoff inside the scheduler.

Algorithmic approach

Solving the joint problem exactly is intractable: it is a mixed discrete optimization with strong temporal coupling, and decisions must be made causally since future SINR realizations are unknown. The proposed method therefore approximates it with a two-level heuristic:

• Release-aware greedy DAG scheduler (RADG): given release times, jobs are processed in topological order and assigned to the core yielding the earliest completion time, respecting precedence, core availability, and release constraints. One pass costs $O(C(V+E))$.
• One-step rollout sensing rule: at each slot, every feasible unfinished band is tentatively activated; if this completes a band's sensing, its entry-job release time is updated, RADG is re-run, and the band minimizing the estimated makespan is selected. Total complexity is $O(TKC(V+E))$ versus $O(TK + C(V+E))$ for the decoupled baseline.

The decoupled baseline allocates each slot to the feasible band with the largest residual information demand—a purely sensing-driven rule—and schedules the DAG only after all bands complete, releasing all entry jobs simultaneously at $\max_k \tau_k$. The comparison thus isolates precisely the value of release-awareness in the sensing policy.

Simulation findings

The evaluation uses $K=6$ bands feeding six branches (node counts $[5,6,7,6,8,6]$) merged through two alignment nodes and a fusion head, with $\max_v f_v$0 cores by default, node latencies uniform in $\max_v f_v$1 ms, and off-chip access costs roughly an order of magnitude above on-chip costs.

Three qualitative results emerge from the timeline analysis. Under joint scheduling, branch execution begins immediately upon band completion, whereas the baseline delays all entry nodes until the last band finishes. Joint scheduling also produces release patterns better aligned with core availability, because each sensing decision accounts for downstream makespan impact.

Quantitatively, the gains are regime-dependent rather than universal:

Setting	Best gain	Regime
Heterogeneous thresholds, moderate data ($\max_v f_v$2)	+13.07%	Strongly coupled
Halved data sizes ($\max_v f_v$3)	+18.16%	Sensing-constrained
Mixed branch depths $\max_v f_v$4 ($\max_v f_v$5)	+18.16%	Staggered releases
Uniform slices $\max_v f_v$6 kB	≤ +1.47%	Weak heterogeneity
5× data sizes, $\max_v f_v$7	−3.92%	Decoupled wins
Reduced to 3 active slices, $\max_v f_v$8	−6.19%	Decoupled wins

Two patterns deserve emphasis. First, the largest improvements occur when slice heterogeneity is high and sensing is the binding constraint—the conditions under which staggered branch releases create exploitable overlap. Second, and notably, the paper does not claim uniform superiority: at large core counts or with few active slices, the greedy one-step rollout can be slightly outperformed by the simple backlog-driven baseline, indicating that abundant compute or weak cross-branch coupling shifts the bottleneck to global ordering effects that a one-step look-ahead cannot capture. Core scaling also saturates beyond roughly 4–6 cores, as the critical path migrates from per-core contention to alignment/fusion synchronization.

Limitations and open questions

The paper is explicit about several caveats. The rollout heuristic carries no performance guarantee relative to the optimal solution of the joint problem, and its one-step horizon means sensing decisions are myopic with respect to future SINR evolution—consistent with the observed losses at high core counts. The evaluation relies on synthetic SINR processes (uniform in $\max_v f_v$9 dB), randomly generated node latencies, and a single representative DAG topology, so the boundary between gain and loss regimes is characterized empirically rather than analytically. The mapping-dependent latency model assumes on-chip forwarding only within same-core, same-branch pairs, which may not reflect accelerator architectures with shared on-chip buffers across cores. Open questions include whether multi-step or learned look-ahead policies can eliminate the regressions observed in low-coupling regimes, and what analytical conditions on threshold heterogeneity and branch depth predict when release-aware scheduling strictly dominates.

Conclusion

The paper contributes a unified timing model linking radar band completion to DNN branch release times, a tractable release-aware greedy scheduler exploiting this link, and a sensitivity analysis that delineates when cross-stage parallelism pays off. Latency reductions of up to approximately 18% are achieved in heterogeneous, sensing-constrained settings, while the honest reporting of cases where decoupled scheduling wins clarifies the operating envelope of the approach and motivates schedulers with stronger guarantees.