analiza aiqbench
Differentiable Weightless Controllers (DWCs) offer a promising hybrid architecture that bridges the gap between the efficiency of symbolic logic and the flexibility of deep learning for autonomous control. By training end-to-end via gradients while compiling to FPGA-compatible circuits, DWCs achieve single-clock-cycle latency and nanojoule-level energy costs without sacrificing performance on high-dimensional benchmarks. This approach also provides structurally sparse, interpretable connectivity patterns, allowing engineers to directly inspect decision logic—a significant advantage for safety-critical real-world applications.
Differentiable Weightless Controllers: Learning Logic Circuits for Continuous Control
arXiv:2512.01467v2 Announce Type: replace Abstract: Controlling autonomous systems under real-world conditions often requires policies that can be evaluated with low latency and minimal energy consumption. Unfortunately, these conditions are at odds with the use of high-precision deep neural networks as controllers. In this work, we introduce Differentiable Weightless Controllers (DWCs), a symbolic-differentiable architecture that learns flexible, non-linear, yet highly efficient control policies. DWCs can be trained end-to-end via gradient-based techniques, yet compile directly into FPGA-compatible circuits with few- or even single-clock-cycle latency and nanojoule-level energy cost per action. Across five MuJoCo benchmarks, including high-dimensional Humanoid, DWCs achieve returns competitive with standard deep policies (full-precision or quantized neural networks). Furthermore, DWCs exhibit structurally sparse and interpretable connectivity patterns, enabling direct inspection of which input values influence control decisions.
Abstract:Controlling autonomous systems under real-world conditions often requires policies that can be evaluated with low latency and minimal energy consumption. Unfortunately, these conditions are at odds with the use of high-precision deep neural networks as controllers. In this work, we introduce Differentiable Weightless Controllers (DWCs), a symbolic-differentiable architecture that learns flexible, non-linear, yet highly efficient control policies. DWCs can be trained end-to-end via gradient-based techniques, yet compile directly into FPGA-compatible circuits with few- or even single-clock-cycle latency and nanojoule-level energy cost per action. Across five MuJoCo benchmarks, including high-dimensional Humanoid, DWCs achieve returns competitive with standard deep policies (full-precision or quantized neural networks). Furthermore, DWCs exhibit structurally sparse and interpretable connectivity patterns, enabling direct inspection of which input values influence control decisions.
Machine Learning (cs.LG); Hardware Architecture (cs.AR); Symbolic Computation (cs.SC)
arXiv:2512.01467 [cs.LG]
(or arXiv:2512.01467v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2512.01467 Focus to learn more arXiv-issued DOI via DataCite
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