arXiv subjects

Amirreza Khorasanian

Publications and source records attributed to Amirreza Khorasanian.

4 recordsLinked to original sources

arXiv2026

Certified Bank-Aware Fast-Path Coverage for Hardware-Constrained NDN Forwarding Lookup

Named Data Networking (NDN) replaces address-based forwarding with longest-prefix lookup over variable-length hierarchical names, creating a difficult fit with bounded hardware fast paths. We formulate this problem as certified fast-path coverage. An offline mapper selects a prefix-closed set of parser-visible trie nodes and a separate set of action-equivalence certificates under a shared budget. A certificate relates a selected shallow FIB prefix to a deeper true-LPM class with the same forwarding action; applying the shortcut requires a sound class guard, while uncertain traffic falls back to an exact path. We present BPC-Greedy, a certificate-aware placement heuristic, and BPC-Portfolio, a multi-strategy wrapper spanning exact-heavy, repair-based, and shallow-shortcut constructions. The formulation exposes a tree-closure knapsack structure for prefix placement, exact top-k certificate selection for a fixed selected set under uniform certificate cost, and a separate bank-feasibility layer. On a synthetic parser-depth sweep and a 50k replay of public NDN testbed names under a reproducible synthetic forwarding-action snapshot, modeled certified coverage increases from 46.48% for the best exact-prefix-only bank-aware baseline to 64.06% at total budget M=48. These results characterize placement opportunity under the stated model: the evaluator uses the known true LPM as an oracle-style guard, certificate-table bank traffic is not modeled, and no claim is made about synthesized latency, area, or throughput.

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arXiv2026

CAGE: Regime-Aware Algorithm Engineering for Coverage Optimization

Coverage-selection policies that optimize the same monotone objective can occupy different quality-runtime frontiers as workload scale, budget, repetition horizon, and execution substrate change. CAGE is a regime-aware algorithm-engineering framework that holds coverage semantics fixed through a shared bitset execution substrate and studies how the nondominated strategy set changes under controlled operating conditions. We separate intrinsic workload descriptors from measured strategy response, represent each condition by its empirical Pareto set, and identify regime transitions from changes in Pareto membership or statistically supported crossovers. Across Defects4J, Epinions, a BWSN-inspired time-to-detection workload, repeated selection, and an embedded RV32 realization, we observe distinct transitions. At a 5% budget, one-exchange refinement adds 27 Chart lines at 11.3x baseline median runtime, whereas deeper search adds five Time lines at roughly 715x. On Math, CP-SAT confirms full-universe optimality at 20% and 40% and residual lower-budget headroom. Persistent preprocessing reuse first achieves confidence-interval-supported speedup at horizon 20 and reaches 1.442x at horizon 100. ModelSim shows a separate bitset-width crossover. The results demonstrate that strategy nondominance is workload- and condition-dependent, supporting regime-aware algorithm engineering rather than a universal policy ranking.

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arXiv2026

MeshReduce-U: Compiler-Guided Communication Reduction for Irregular Neural Reductions on Mesh NoCs

Many irregular neural workloads induce skewed many-to-one reductions with repeated neighborhoods and nonlocal communication. Conventional NoC mappers optimize placement and routes for a fixed communication graph, even though associative reductions expose legal opportunities to eliminate traffic before routing. We present MeshReduce-U, a compiler-guided communication-reduction and routing framework for mesh-NoC-based spatial accelerators. MeshReduce-U coalesces colocated sources, forms local aggregation islands, blocks channels with compatible fan-in structure, selects capacity-feasible sinks, and routes the remaining fixed-width carriers using fused usage-aware costs. A deterministic route-replay model reports schedule-derived communication latency, total link usage (TLU), and fused link usage (FusedTLU) separately. Across a 20-workload lowerable neural-network zoo, MeshReduce-U reduces mean latency, TLU, and FusedTLU by 40.3%, 56.0%, and 48.7%, respectively, relative to an ABC-style source-aggregation baseline, improving all three metrics on every workload. Across 40 synthetic irregular reductions, it reduces mean latency and TLU by 12.3% and 19.7%. A new 30-instance pass-by-pass study further shows that the structural rewrites reduce the global carrier count by 60.9% and replay latency by 63.0%. These results show that rewriting reducible neural communication before routing can be more effective than searching harder over an unreduced traffic graph.

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arXiv2026

Beyond Edge Cuts: Activity-Weighted Multicast Hypergraph Mapping for Spiking Neural Networks on Mesh NoCs

Mapping spiking neural networks (SNNs) onto neuromorphic many-core platforms is often formulated with graph partitioning and pairwise placement costs. That abstraction is convenient, but it does not match the physical communication event: one spike from a source neuron is delivered to a set of postsynaptic destinations, and routes to several destinations can share mesh links. We present M-HySMap, a route-aware, activity-weighted multicast hypergraph mapping framework. Each source neuron induces a directed hyperedge to its postsynaptic fanout, weighted by profiled activity. The mapper starts from strong activity-aware graph/QAP seeds and then optimizes distinct destination-core fanout, the union of deterministic mesh routes, and link congestion. The central algorithmic observation is locality: moving one neuron can change only its own source-rooted hyperedge and the hyperedges of its predecessors. This permits exact incremental gain evaluation while caching every unaffected route contribution. We expose this combinatorial structure in detail, derive a conservative placement lower bound, and describe a portfolio of partition and placement neighborhoods that preserves the best incumbent. Across a 115-job evidence suite on Potjans-inspired recurrent SNNs and mesh NoCs from 4 x 4 to 6 x 6, plus a 7 x 7 stress case, M-HySMap reduces routed multicast hops by 10.6-19.6% over Activity+QAP and 19.7-41.1% over Edge+QAP. Incremental updates accelerate refinement by 4.7-12.7x while matching full recomputation to numerical precision.

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