Collision detection can account for over 90% of the total calculation time in motion planning, which makes it critical to optimize the collision detection process. Previous hardware accelerators can hardly maintain fast computation speed for real-time collision detection while supporting a large roadmap. In this work, we present a novel in-memory-search accelerator. hasub-10 μJs time for collision detection in 800 MB scale roadmaps with 100,000 edges. Such accelerator leverages an in-memory-search architecture that can perform search operation in massively parallel manner with low energy consumption. It is powered by ternary content-addressable memories (TCAMs) based on large-scale nonvolatile resistive random-access memory (RRAM) arrays, which not only store a large roadmap on chip, but also perform look-up table function in a single clock cycle to accelerate collision detection by combining with the unique logic encoding scheme. Importantly, the RRAM-based TCAM eliminates the need for extensive data transfer between memory and computing units during collision detection, leading to significant energy and delay saving. As the pioneering TCAM-based in-memory-search accelerator for collision detection, the structure achieves 210.2,225× times speedup compared with previous ASIC-based accelerators, and more than 33,879 times speedup compared with CPU-based collision detection. Such accelerator well exceeds the speed requirement for collision detection (<1 ms), and is suitable for a number of applications such as robot motion planning in dynamic environment, animation, machining and manufacturing.

Universal sound separation (USS) is a task to separate arbitrary sounds from an audio mixture. Existing USS systems are capable of separating arbitrary sources, given a few examples of the target sources as queries. However, separating arbitrary sounds with a single system is challenging, and the robustness is not always guaranteed. In this work, we propose audio prompt tuning (APT), a simple yet effective approach to enhance existing USS systems. Specifically, APT improves the separation performance of specific sources through training a small number of prompt parameters with limited audio samples, while maintaining the generalization of the USS model by keeping its parameters frozen. We evaluate the proposed method on MUSDB18 and ESC-50 datasets. Compared with the baseline model, APT can improve the signal-to-distortion ratio performance by 0.67 dB and 2.06 dB using the full training set of two datasets. Moreover, APT with only 5 audio samples even outperforms the baseline systems utilizing full training data on the ESC-50 dataset, indicating the great potential of few-shot APT.

Access to large corpora with strongly labelled sound events is expensive and difficult in engineering applications. Many researches turn to address the problem of how to detect both the types and the timestamps of sound events with weak labels that only specify the types. This task can be treated as a multiple instance learning (MIL) problem, and a key to it in the sound event detection (SED) task is the design of a pooling function. The linear softmax pooling function achieves state-of-the-art performance since it can vary both the signs and the magnitudes of gradients. However, linear softmax pooling cannot flexibly deal with sound events of different time scales. In this paper, we propose a power pooling function which can automatically adapt to various sound events. By adding a trainable parameter to each event, power pooling can provide more accurate gradients for frames in a clip than other pooling functions. On both weakly supervised and semi-supervised SED datasets, the proposed power pooling function outperforms linear softmax pooling on both coarse-grained and fine-grained metrics. Specifically, it improves the event-based F1 score by 11.4% and 10.2% relatively on the two datasets. While this paper focuses on SED applications, the proposed method can be applied to MIL tasks in other domains.