A novel defect-tolerant network of digital-to-analog converters (DACs) is presented in this paper. The architecture of this converter employs a single 2.5-V voltage reference and an unbalanced buffering technique to achieve a wide voltage range that extends from 864 mV to 2.538 V with an 8-bit resolution. The proposed converter incorporates a defect-tolerant architecture and is extremely compact, utilizing a per-bit silicon area of less than 350 μm2. Although such very small area allows for embedding in dense configurable fabrics (field-programmable gate arrays) and wafer-scale integration, the overall performance is not sacrificed as reported measurements show a signal-tonoise ratio of 51.87 dB and a spurious-free dynamic range of 42.31 dB, at 10 MS/s providing 7.6 effective bits. Moreover, the proposed architecture benefits from dynamic calibration capabilities, as any converter output can be finely adjusted over a range of 25 mV. This proposed DAC is also extensively reused in the same defect-tolerant network for a successive approximation register-analog-to-digital converter, as well as for a configurable voltage reference.

Finding optimal/short-span Convolutional Self-Doubly Orthogonal (CDO) codes and Simplified-CDO (S-CDO) codes for a specified order J is computationally very challenging. This paper describes several optimizations that were applied to an implicitly-exhaustive search algorithm in order to reduce the time required for finding these types of codes. The resulting high-performance parallel implementation provides an impressive speedup that is greater than 16 300 (CDO,${\rm J} = 7$) and 6300 (S-CDO,${\rm J} = 8$) over the reference implicitly-exhaustive search algorithm, and greater than 2000$({\rm J} = 17)$over the fastest published CDO validation function used in high-performance pseudorandom search algorithms. These speedups are achieved through enhancements in the deterministic search-space reduction, and a vastly improved validation function that makes use of a novel data structure for enabling data-reuse and incremental computations. The resulting validation function speedup is greater than 60 000 (S-CDO,${\rm J} = 17$) and 190 000 (CDO,${\rm J} = 17$) when compared to its reference implementation. The combination of optimizations and load-balancing techniques allowed us to leverage hundreds of processor cores in order to complete an exhaustive search over a search space that is some$10^{14}$times larger than what was previously possible.

A novel parallel and implicitly-exhaustive search algorithm for finding, in systematic form, rate R=1/2 optimal-span Convolutional Self-Doubly Orthogonal (CDO) codes and Simplified Convolutional Self-Doubly Orthogonal (S-CDO) codes is presented. In order to obtain high-performance low-latency codecs with these codes, it is important to minimize their constraint length (or "span") for a given J number of generator connections. The proposed exhaustive algorithm uses algorithmic enhancements over the best previously published searching techniques, yielding new and improved codes: we were able to obtain new optimal-span CDO/S-CDO codes (having order J∈{9} and J∈{10,11} respectively), as well as new codes having the shortest spans published to date for higher values of J (J∈{10,12,...,17} and J∈{12,...,20} for CDO and S-CDO codes respectively). The new codes and their error performance are provided. An analysis of the evolution of the CDO/S-CDO code error performance as J increases is presented, and the shortest CDO/S-CDO code span values for each given J are compared.

A novel implicitly-exhaustive search algorithm for finding, in systematic form, rate R=\frac{1}{2} optimal-span Convolutional Self-Doubly Orthogonal (CDO) codes and Simplified Convolutional Self-Doubly Orthogonal (S-CDO) codes is presented. In order to build high-performance low-latency codecs with these codes, it is important to minimize their constraint length (or "span") for a given J number of generator connections. The proposed algorithm is exhaustive in nature and its improvements over the best previously published searching techniques allowed it to yield new optimal-span CDO/S-CDO codes (having order J ∈ {6,7,8} and J ∈ {9} respectively), as well as a span reduction for codes with a higher J value (J ∈ {10,11} and J ∈ {14,15} for CDO and S-CDO respectively).