Accurate 3D object detection (3D-OD) is critical for autonomous vehicles, yet embedded platforms impose strict latency, power, and memory constraints. While LiDAR–camera fusion improves robustness, existing approaches depend on precise calibration and computationally expensive view projections. We present MAPLE, a projection-free and calibration-resilient fusion framework that adaptively balances LiDAR geometry and camera semantics using Gated Confidence Fusion (GCF) and low-rank adapter (LoRA) enhanced global attention refinement. MAPLE preserves fine-grained cross-modal interactions without view lifting and injects long-range context at low cost. On the nuScenes benchmark, MAPLE improves mean Average Precision (mAP) by up to 1.6% over the strongest prior fusion baseline, while reducing inference latency by 42.6% and energy consumption by 47% on the NVIDIA Jetson Orin Nano, demonstrating suitability for real-time embedded autonomous perception.

To enhance perception in autonomous vehicles (AVs), recent efforts are concentrating on 3D object detectors, which deliver more comprehensive predictions than traditional 2D object detectors, at the cost of increased memory footprint and computational resource usage. We present a novel framework called UPAQ, which leverages semi-structured pattern pruning and quantization to improve the efficiency of LiDAR point-cloud and camera-based 3D object detectors on resource-constrained embedded AV platforms. Experimental results on the Jetson Orin Nano embedded platform indicate that UPAQ achieves up to 5.62× and 5.13× model compression rates, up to 1.97× and 1.86× boost in inference speed, and up to 2.07× and 1.87× reduction in energy consumption compared to state-of-the-art model compression frameworks, on the Pointpillar and SMOKE models respectively.

Recent advances in machine learning (ML) have spotlighted the pressing need for computing architectures that bridge the gap between memory bandwidth and processing power. The advent of deep neural networks has pushed traditional Von Neumann architectures to their limits due to the high latency and energy consumption costs associated with data movement between the processor and memory for these workloads. One of the solutions to overcome this bottleneck is to perform computation within the main memory through processing-in-memory (PIM), thereby limiting data movement and the costs associated with it. However, dynamic random-access memory-based PIM struggles to achieve high throughput and energy efficiency due to internal data movement bottlenecks and the need for frequent refresh operations. In this work, we introduce OPIMA, a PIM-based ML accelerator, architected within an optical main memory. OPIMA has been designed to leverage the inherent massive parallelism within main memory while performing high-speed, low-energy optical computation to accelerate ML models based on convolutional neural networks. We present a comprehensive analysis of OPIMA to guide design choices and operational mechanisms. In addition, we evaluate the performance and energy consumption of OPIMA, comparing it with conventional electronic computing systems and emerging photonic PIM architectures. The experimental results show that OPIMA can achieve$2.98\times $higher throughput and$137\times $better energy efficiency than the best known prior work.

Object detectors used in autonomous vehicles can have high memory and computational overheads. In this paper, we introduce a novel semi-structured pruning framework called R-TOSS that overcomes the shortcomings of state-of-the-art model pruning techniques. Experimental results on the JetsonTX2 platform show that R-TOSS has a compression rate of 4.4× on the YOLOv5 object detector with a 2.15× speedup in inference time and 57.01% decrease in energy usage. R-TOSS also enables 2.89× compression on RetinaNet with a 1.86× speedup in inference time and 56.31% decrease in energy usage. We also demonstrate significant improvements compared to various state-of-the-art pruning techniques.