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The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles. Most of the high-accuracy techniques for this task are based on R-CNN or one of its faster variants. In the research community, much emphasis has been applied to using 3D vision or complex R-CNN variants to achieve(More)
—We present the Berkeley Model and Algorithm Prototyping Platform (MAPP), a MATLAB R-based framework for conveniently and quickly prototyping device compact models and simulation algorithms. MAPP's internal code structuring, which differs markedly from that of Berkeley SPICE and related simulators, allows users to add new devices with only minimal knowledge(More)
We propose a new metric for quantifying per-element distortion that is simple, intuitive and well-defined for both small- and large-signal excitations. Traditional distortion concepts, based on polynomial expansions and Volterra series, can be viewed as an approximation of our new metric. Although computing this metric exactly is quadratic in circuit size,(More)
Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment. In this work, we propose SqueezeDet, a(More)
The great availability of massively parallel computing platforms gives rise a question to the EDA industry--how can this be really helping the productivity of circuit designs. Scalability of traditional parallel methods have shown to be limited as the computational resources keep increasing. In this paper we propose a time-domain segmentation method for(More)
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