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The authors propose Terracotta, a new framework that consists of two flexible components: (i) custom command extensions that enable DRAM vendors to define new commands within a single, standardized interface, and (ii) a programmable memory controller that system designers can easily and flexibly program to support new DRAM techniques post-silicon fabrication.
This work presents a framework for accelerating transformer-based language models (LMs) on resource-constrained IoT devices. The framework targets compact LMs: BERT-Tiny (B-Ty), MobileBERT (M-Bt), MiniLM (M-Lm), Electra (E-Lt) and DeBERTa (D-Bt) -- selected for their architectural diversity and use in edge inference scenarios.
While dependent quantization in H.266/VVC delivers a high compression ratio, its strong serial nature and high complexity result in poor real-time performance, making it difficult to deploy in practical scenarios. To improve the real-time performance of dependent quantization with minimal degradation to its compression performance, the authors propose a interleaved parallel dependent quantization hardware architecture with low BDBR loss, which achieves four-channel parallel dependent quantization by time-division multiplexing most combinational logic.
This paper presents the first security analysis of the CAN XL standard, focusing on its MAC sub-layer. The authors develop a bit-precise CAN XL formal model and release it to facilitate future research.
This paper presents the first dToF LiDAR system-on-chip (SoC) with integrated sensor-level hardware security against spoofing attacks.
This paper presents ZTA-Q, an open-source RISC-V-based platform that enables accurate deployment of TensorFlow Lite INT8 models.
