This paper presents an approach to the mitigation of harmonic distortions in wideband current-steering digital-to-analog converters (DACs). This approach enables code-independent constant-switching-activity data-weighted-averaging (CSA-DWA) with the extra area and power overhead by exploiting redundant current sources. With CSA-DWA, a 16-bit 4.0-GS/s calibration-free DAC is designed in 65 nm CMOS. To achieve high-speed low-complexity CSA-DWA decoding, the most-significant-bit (MSB) segment is set to 5 bits. The MSB switching activities are regulated to be constant with 1-bit randomized switching activity to minimize the non-linearity due to the MSB switching activity truncation errors in the CSA-DWA decoder. Furthermore, a power delivery scheme is adopted to reduce the IR-drop mismatch between the switching elements. Experimental results show that this DAC achieves$>$70 dBc spurious-free dynamic range (SFDR) and$< -80$dBc third-order intermodulation distortion (IM3) up to 1 GHz. With the proposed CSA-DWA, SFDR and IM3 are improved by 4–15 dB and 5–14 dB, respectively, across the Nyquist band.

Time interleaving (TI) is an effective approach to higher speed conversion of current-steering digital-to-analog converters (DACs). However, achieving high linearity performance for these TI DACs is challenging during interleaving synchronization, output current summation, and parasitic capacitance control. This article exploits the design of a 6.0-GS/s 14-bit two-channel time-interleaved DAC in a 65-nm CMOS process for communication systems. A novel asymmetric current-tree summation network is proposed to reduce the current summation nonlinearity in the DAC. A differential clock phase and duty-cycle calibration scheme is also adopted while achieving low complexity. Furthermore, a current source layout optimization scheme is proposed that significantly reduces the parasitic capacitance of interleaving switches and improves the linearity. Measurement results of the fabricated DAC show 6–20-dB spurious-free dynamic range (SFDR) improvement with the proposed techniques, achieving >60-dB SFDR up to 1355 MHz and >50-dB SFDR up to the Nyquist.

This paper presents dynamic switching sequence (DSS) for current-steering digital-to-analog converters (DACs). Unlike conventional static switching sequence (SSS), the proposed DSS dynamically selects from pre-defined switching sequences, and achieves a minimized integral nonlinearity (INL) that is even lower than the lower bound of traditional SSS. Moreover, it works effectively with the digital pre-distortion (DPD) technique to further reduce the residual nonlinearity. Simulation results of a 16-bit segmented DAC show an average INL reduction from 37.9 LSBs (SSS, with 17.7 LSBs as the lower bound) and 10.3 LSBs (SSS+DPD, normalized), to 8.2 LSBs (with DSS) and 0.2 LSBs (DSS+DPD).

Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limits the flexibility of network patterns in learned cell structures. Moreover, due to the topology-agnostic nature of existing works, including both cell-based and node-based approaches, the search process is time consuming and the performance of found architecture may be sub-optimal. To address these problems, we propose AutoShrink, a topology-aware Neural Architecture Search (NAS) for searching efficient building blocks of neural architectures. Our method is node-based and thus can learn flexible network patterns in cell structures within a topological search space. Directed Acyclic Graphs (DAGs) are used to abstract DNN architectures and progressively optimize the cell structure through edge shrinking. As the search space intrinsically reduces as the edges are progressively shrunk, AutoShrink explores more flexible search space with even less search time. We evaluate AutoShrink on image classification and language tasks by crafting ShrinkCNN and ShrinkRNN models. ShrinkCNN is able to achieve up to 48% parameter reduction and save 34% Multiply-Accumulates (MACs) on ImageNet-1K with comparable accuracy of state-of-the-art (SOTA) models. Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1.5 GPU hours, which is 7.2× and 6.7× faster than the crafting time of SOTA CNN and RNN models, respectively.