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Coding What Matters: A Semantic-Aware Memory Interface for Energy-Efficient Perception in Autonomous Vehicles

Haohua Que, Handong Yao

arXiv:2608.29000v1cs.RO

TL;DR

Autonomous vehicles spend energy moving camera frames through memory according to bit-1 density and bus switching, not pixel semantics. MotiMem-Omega allocates precision by semantic importance and reduces front-camera bit-1 density by about 52%, corresponding to a modeled memory-interface energy reduction of 36% and 27% under the swept coefficient range.

  • Problem

    The camera-to-memory interface consumes energy based on stored ones and adjacent-bit switching, while existing allocation need not reflect the differing importance of road users, background, and sky.

  • Method

    MotiMem-Omega builds a safety-aware semantic importance field and assigns each image block an energy-optimal precision, optionally propagating protected regions with ego-compensated motion.

  • Results

    52% lower front-camera bit-1 density maps to a 36% memory-interface energy reduction, with a lower end of 27% across swept literature coefficients, while savings remain consistent across evaluated perception tasks and datasets.

  • Takeaways & Limitations

    Semantic memory coding targets camera-to-memory traffic rather than model computation or compressed-file size, preserving perception accuracy while reducing interface-energy proxies that image codecs do not reduce.

  • Takeaways & Limitations

    Energy savings are analytically modeled from measured bit-1 density and switching with literature coefficients, not directly measured in silicon watts, and apply only to the perception input data path.

Abstract

from arXiv · show

Autonomous vehicles stream high-resolution surround-camera frames into memory before perception runs. This sensor-to-memory path consumes energy when cells store ones and adjacent bytes toggle on the data bus, so its cost follows bit-1 density and switching activity rather than pixel semantics. We present MotiMem-Omega, a semantic-aware memory-interface coder that lowers this cost while preserving perception predictions. Its semantic importance field protects traffic participants, especially vulnerable road users, while assigning lower fidelity to sky and empty background. Cross-dataset bit-sensitivity sweeps determine class weights, with a safety floor for pedestrians, cyclists, and motorcyclists. Each image block then selects a precision tier by minimizing a joint energy-distortion cost. When ego pose is available, a motion-compensated prior carries protected regions between frames. We estimate interface-energy reduction from the two measured proxies using a coefficient-swept memory-energy model. Across 29 detectors on 12 driving datasets, 5 occupancy models, and 5 segmentation networks, MotiMem-Omega retains about 90% of detection mean average precision, 91% of vulnerable-road-user recall, over 98% of occupancy accuracy, and the strongest segmentation retention among energy-reducing methods. It reduces front-camera bit-1 density by 52%, corresponding to a modeled memory-interface energy reduction near 36%, with a lower end of 27% under the literature coefficient sweep. It also gives higher retention than the baseline and energy-matched truncation at the same or lower bit-1 density, whereas image codecs preserve accuracy without reducing memory-interface energy.

I. INTRODUCTION

MotiMem-Ω targets sensor-to-memory interface energy, which depends on stored bit patterns and switching rather than pixel semantics. It allocates fidelity according to scene importance and evaluates the approach across diverse perception models and datasets.

  • Motivation: Interface energy grows with stored bit-1 density and adjacent-bit switching, while conventional efficiency methods largely optimize compute or file size.These byte-level costs differ from the semantic importance of pedestrians, vehicles, roads, and sky.
  • Results: About 90% mAP retention is achieved at normalized bit-1 density ρ1 ≈0.48, while energy-matched truncation and the baseline lose 7 to 10 points at the same or higher energy.Image codecs preserve accuracy but remain at ρ1 ≥1, providing no memory saving.
  • Core idea: MotiMem-Ω assigns higher protection to traffic participants, especially vulnerable road users, and lower fidelity to sky and empty background.Road and ground regions receive conservative precision because undetected or distant road users may appear there.
  • Core idea: Each image block selects an energy-optimal precision using a semantic importance field derived from detections and a sky prior.The field uses cross-dataset class sensitivity, a vulnerable-road-user safety floor, and motion-compensated priors when ego pose is available.
  • Evaluation: The evaluation covers 29 detectors on 12 driving datasets, alongside 5 occupancy models and 5 segmentation networks, against about ten encoding baselines.Retention compares decoded-frame predictions with predictions from the original frame.

II. RELATED WORK

Prior work reduces compute, file size, or generic memory cost, but does not jointly target the camera-to-memory interface with scene-aware fidelity allocation. MotiMem-Ω addresses this gap through semantic, safety-aware storage coding validated across heterogeneous perception tasks.

  • Existing approaches: Approximate-memory and interconnect methods target bit transitions or storage power, while model-efficiency methods reduce compute and parameter movement after frame storage.These approaches generally treat stored bytes uniformly rather than allocating fidelity by scene semantics.
  • Existing approaches: Image and video codecs preserve machine-task accuracy or reduce file size, but entropy-coded streams do not explicitly minimize memory-interface bit density or switching.A smaller transported file therefore does not imply lower energy while the frame resides in memory.
  • Research gap: The identified gap requires targeting the camera-to-memory interface, differentiating safety-critical road users from low-value background, and validating across heterogeneous tasks and datasets.MotiMem-Ω is designed around all three requirements.
  • MotiMem-Ω: MotiMem-Ω reshapes stored camera bytes using a data-driven semantic field and a vulnerable-road-user safety floor to decide where precision is spent.Its objective is lower bit-1 density and switching rather than human-perceived image quality.

III. PROBLEM FORMULATION

The formulation inserts an encoder and decoder around the camera frame buffer and evaluates storage-path cost through bit-1 density and switching. Fidelity is constrained by agreement between decoded and original-frame perception predictions across models and classes.

  • System model: The encoder maps the current frame and previous-frame detections to a stored byte stream, which the decoder reconstructs before perception runs.This places the coding operation directly on the DRAM/SRAM frame-buffer path.
  • Energy model: The interface-energy proxy uses normalized bit-1 density ρ1 and switching activity α, with ρ1 capturing cell-array and bus-inversion energy and α capturing I/O-bus toggling.The coefficients decompose the modeled energy contribution of these terms.
  • Optimization objective: MotiMem-Ω optimizes the encoder subject to a tolerated accuracy loss ε between predictions on decoded and original frames.The constraint is intended to hold across every model and class rather than one detector.
  • Optimization objective: Because the constraint spans models and classes, the encoder must allocate fidelity using a model-agnostic, safety-aware notion of pixel importance.This requirement motivates the semantic importance field used by the method.

IV. THE MOTIMEM-ΩMETHOD

The MotiMem-Ω pipeline constructs a semantic prior, allocates precision through sky-aware hierarchical tiers, and codes the resulting bytes before decoding and perception.

  • Semantic-prior construction: Semantic-prior construction combines the input frame, previous detections, motion projection, and a semantic field.This stage identifies where precision should be protected.
  • Scene-prior precision allocation: Scene-prior precision allocation uses a sky prior and hierarchical tiers to determine regional coding precision.The allocation stage follows semantic-prior construction and precedes byte-level storage coding.
  • Byte-level memory interface: The byte-level interface stores the coded frame, measures frame-buffer energy, decodes it, and passes the reconstruction to closed-loop perception.The three stages form a pipeline from scene context to stored bytes and downstream predictions.

A. Semantic Prior Construction

MotiMem-Ω builds a block-level semantic importance field from prior detections, ego-compensated motion, class criticality, and safety-aware region tiers.

  • A. Semantic Prior Construction: The encoder uses the current frame and previous decoded-frame detections to identify where precision should be protected.When ego pose is available, prior detections are projected forward using an ego-compensated motion model.
  • A. Semantic Prior Construction: Class importance combines data-driven criticality, object scale and position, and detection confidence.Pedestrians, cyclists, motorcyclists, and vehicles receive high priority, while context and static background receive lower priority.
  • A. Semantic Prior Construction: Cross-dataset bit sensitivity learns class weights while imposing a floor for vulnerable road users.This protects pedestrians, cyclists, and motorcyclists even when measured sensitivity would otherwise assign lower weights.
  • A. Semantic Prior Construction: Dilated detections are converted into a block field whose maximum covered importance defines ordered semantic tiers.The calibrated tiers separate vulnerable-road-user and safety-critical blocks, vehicles, context/background, and sky.

B. Scene Priors and Precision Allocation

The coder adds a separate sky prior and maps semantic tiers to precision through an energy–distortion allocation, with adaptive object-block modes trading distortion against switching.

  • B. Scene Priors and Precision Allocation: A lightweight sky segmentation branch assigns sky blocks to the lowest-importance tier while retaining road and ground in the context tier.Road and ground remain more conservative because distant or missed vulnerable road users can appear there.
  • B. Scene Priors and Precision Allocation: The combined semantic and sky maps form a multi-tier block map rather than a binary object/background mask.Tier-specific precisions include kobj for object tiers, kbg for context/background, and ksky for sky.
  • B. Scene Priors and Precision Allocation: Each tier’s precision is selected by balancing measured bit-1 density and distortion according to its area and importance weight.The allocation drives tiers toward a common marginal energy-per-distortion before calibrated tiers are tied.
  • B. Scene Priors and Precision Allocation: Object blocks adaptively choose among preserve, truncate, and adaptive modes by minimizing distortion plus λ-weighted switching.Background and sky use coarser fixed precisions, and the two-tier conference scheme is a special case of the proposed hierarchy.

C. Byte-Level Memory Interface

MotiMem-Ω converts selected precisions into stored bytes using Gray coding and selective inversion, then measures bit-1 density and switching activity as interface-energy proxies.

  • C. Byte-Level Memory Interface: For precision k, the coder Gray-codes the top k bits, selectively inverts majority-one codewords, and stores the inversion flag in the least-significant bit.This byte-level stage turns the tier decision into the actual frame-buffer representation.
  • C. Byte-Level Memory Interface: Gray coding and selective inversion reduce transitions and majority-one patterns in the stored stream, lowering bit-1 density ρ1 and switching activity α.These are the two byte-level quantities measured at the frame buffer.
  • C. Byte-Level Memory Interface: The encoded stream is written to the frame buffer, decoded on read-out, and passed to the unmodified perception models.Detector output then supplies the next frame’s semantic prior.
  • C. Byte-Level Memory Interface: The frame-buffer meter records ρ1 and α because charged memory cells and bus activity contribute asymmetrically to DRAM and SRAM energy.The measured proxies connect the byte coder to modeled memory-interface energy.

A. Setup and Implementation Details

The evaluation calibrates MotiMem-Ω across broad detection and dense-perception benchmarks, selecting operating points that trade small retention losses for lower energy proxies and semantic allocation gains.

  • A. Setup and Implementation Details: The study evaluates 29 detectors across 12 driving datasets, five occupancy models, and five segmentation networks using retention against original-frame predictions.Detection reports mAP and vulnerable-road-user recall, while occupancy and segmentation use task-specific IoU metrics.
  • A. Setup and Implementation Details: kobj=7 preserves a flat ∼98% object-retention plateau while selecting the highest usable precision and an odd-k low-ρ1 point.The usable range is kobj≤7 because the least-significant bit stores the inversion flag.
  • A. Setup and Implementation Details: kbg=5 is selected where retention saturates before the ρ1 increase at kbg=6, while ksky=2 reduces ρ1 from 0.60 to 0.55 for about 3 pp mAP and 1 pp VRU recall.The block-mode energy minimum occurs at λ=1, and VRU recall saturates at 20 px dilation.
  • A. Setup and Implementation Details: MotiMem-Ω retains 0.90 aggregate mAP and 0.91 VRU recall, exceeding the baseline’s 0.83/0.79 while using lower bit-1 density.Its 12-dataset average also exceeds energy-matched naive truncation at comparable density.
  • A. Setup and Implementation Details: MotiMem-Ω preserves more segmentation than energy-matched naive truncation on all five architectures, by +4.9 to +9.0 pp.Across 23 architecture–dataset cells, it wins 22, with one near-tie.
  • A. Setup and Implementation Details: Among energy-reducing methods, MotiMem-Ω has the highest average mAP while codecs match retention only near ρ1≈1.The comparison reports retention relative to original-frame predictions and normalized energy proxies.

E. 3D Occupancy

MotiMem-Ω preserves occupancy performance across five models at substantially lower bit-1 density than the conference baseline. Its advantage is largest for dense, geometry-sensitive perception, where semantic allocation avoids the damage caused by uniform truncation.

  • 3D occupancy: 98.2–99.9% native occupancy-score retention is achieved by MotiMem-Ω at ρ1=0.59, versus 94.8–97.8% for the conference base.The comparison covers five state-of-the-art occupancy models.
  • 3D occupancy: 99.7% versus 94.8% retention on GaussRender marks the widest gap between MotiMem-Ω and the conference base.GaussRender is identified as geometry-sensitive, making it particularly affected by uniform truncation.
  • Cross-task comparison: Dense per-voxel perception benefits more from semantic allocation than sparse detection because uniform truncation flattens background and rewrites voxel fields.The cross-task analysis reports 99.8% versus 95% occupancy retention for MotiMem-Ω and naive truncation.
  • Energy outcome: The derived interface-energy reduction is 30.2% for six-camera occupancy, with a lower end of 23.1% across the swept literature coefficients.Occupancy uses all six surround cameras on nuScenes and ρ1=0.59, a denser setup than the front-camera detection and segmentation evaluation.

G. Design Sensitivity Analysis

Sensitivity analyses show that MotiMem-Ω’s operating point balances retention against bit-level energy proxies, while qualitative and tabular comparisons show stronger retention than competing energy-reducing encoders.

  • Operating-point sensitivity: Removing Gray raises bit-1 density from 0.480 to 0.517, showing that Gray coding primarily contributes energy savings.The sensitivity check isolates the effect of removing Gray coding.
  • Operating-point sensitivity: Keeping sky at background precision recovers 3.2 pp mAP but increases bit-1 density by 7.1% and switching by 23.3%.The full model therefore selects the lower-energy operating point.
  • Operating-point sensitivity: Temporal-prior variants differ by at most 0.003 mAP, indicating that the gain comes from semantic allocation plus sky and byte-level coding rather than a temporal prior alone.The cited sensitivity passage attributes the gain to the combined coding choices.
  • Segmentation comparison: MotiMem-Ω leads segmentation mIoU retention on every architecture, while JPEG matches it only at ρ1≈1 with no saving.Across 12 datasets, DeepLabV3 retention is 0.906 versus 0.858, a +4.8 pp difference.
  • Detection comparison: Across qualitative detection examples, MotiMem-Ω retains objects dropped by the conference base and energy-matched naive truncations.The figure covers 12 datasets, seven encoding methods, and snow, night, and clear scenes.
  • Detection comparison: The conference base can lose objects that moved outside their previous-frame RoI, whereas MotiMem-Ω’s tiered allocation keeps them.Image codecs alter the bitstream but not the predictions in the same comparison.

VI. DISCUSSION AND CONCLUSION

MotiMem-Ω shifts memory-interface coding from content-blind truncation toward semantic allocation, preserving perception across heterogeneous tasks while reducing measured energy proxies. Its benefits are broad but bounded by analytical energy estimates, scene content, and task and deployment assumptions.

  • Cross-task evaluation: 39 models across 12 datasets use one semantic-aware encoded frame without per-model tuning, covering detectors, occupancy, and segmentation networks.The design targets content that these models commonly discard, including flat sky and textureless road.
  • Dense perception: 98.2–99.9% occupancy retention at ρ1=0.6 makes MotiMem-Ω both high-retention and energy-saving, while JPEG matches retention only at ρ1>1.The occupancy comparison spans five state-of-the-art models and contrasts Ω with the conference base, non-semantic truncations, and JPEG.
  • Practical boundaries: The deployment scope is the camera frame buffer and buses, not total-vehicle, neural-network-compute, or actuation energy, and savings depend on visible empty-region content.Urban-canyon scenes retain higher ρ1, while ground and road remain intentionally protected because truncation harms far-VRU recall more than the returned energy justifies.
  • Energy outcome: 52% lower front-camera bit-1 density and 48% lower switching map to a modeled 36% memory-interface energy reduction, with a 27% lower bound across swept coefficients.The reduction is derived from measured ρ1 and switching proxies rather than directly measured silicon watts.
  • Comparative advantage: MotiMem-Ω improves the accuracy–energy operating point over the conference base and energy-matched naive truncations, while codecs preserve accuracy without reducing the memory-interface proxy.Its semantic allocation protects objects and vulnerable road users while moving precision away from low-importance background and sky.
  • Practical boundaries: The motion-compensated prior requires per-dataset ego-pose and falls back to a static region of interest when pose is unavailable.This is a deployment assumption for the temporal protection mechanism, not a limitation of the static semantic coder.
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