Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In such a framework, vector quantization is used for coarse partitioning while product quantization is used for quantizing residuals. In the original IVFADC as well as all of its variants, after residuals are computed, the second production quantization step is completely independent of the first vector quantization step. In this work, we seek to exploit the connection between these two steps when we perform non-exhaustive search. More specifically, we decompose a residual vector locally into two orthogonal components and perform uniform quantization and multiscale quantization to each component respectively. The proposed method, called local orthogonal decomposition, combined with multiscale quantization consistently achieves higher recall than previous methods under the same bitrates. We conduct comprehensive experiments on large scale datasets as well as detailed ablation tests, demonstrating effectiveness of our method.
. ADC transforms inner product computations into a lookup table based operation, which can be implemented in different ways. The original ADC paper @cite_19 used L1 cache based lookup table. @cite_16 used an GPU implementation for ADC lookup. A SIMD based approach was also developed by @cite_26 @cite_13 . Again, this is orthogonal to the local decomposition idea of this work, as any ADC implementation can be used in this work.
@cite_19 @cite_16 @cite_13 @cite_26
2124509324 2593864460 2204555070
Nearest Neighbor (NN) search in high dimension is an important feature in many applications (e.g., image retrieval, multimedia databases). Product Quantization (PQ) is a widely used solution which offers high performance, i.e., low response time while preserving a high accuracy. PQ represents high-dimensional vectors (e.g., image descriptors) by compact codes. Hence, very large databases can be stored in memory, allowing NN queries without resorting to slow I O operations. PQ computes distances to neighbors using cache-resident lookup tables, thus its performance remains limited by (i) the many cache accesses that the algorithm requires, and (ii) its inability to leverage SIMD instructions available on modern CPUs. In this paper, we advocate that cache locality is not sufficient for efficiency. To address these limitations, we design a novel algorithm, PQ Fast Scan, that transforms the cache-resident lookup tables into small tables, sized to fit SIMD registers. This transformation allows (i) in-register lookups in place of cache accesses and (ii) an efficient SIMD implementation. PQ Fast Scan has the exact same accuracy as PQ, while having 4 to 6 times lower response time (e.g., for 25 million vectors, scan time is reduced from 74ms to 13ms). The last years improvements of electronic circuits has allowed the appliance of digital systems in phase noise measurement techniques where low noise and high accuracy are required, yielding flexibility in the implementation and setup of measurement systems. By definition, any measure performed is always affected and limited by the noise of the measurement instrument itself. Considering that the Analog to Digital Converter (ADC) is the core and front end of digital systems, its residual noise has an important impact on the system performance. Consequently, the selection of the proper ADC becomes a critical issue for the system implementation. Currently, the information available in literature deeply describes the ADC features mainly at frequencies offsets far-from-carrier. Nevertheless for time and frequency applications the performance close to the carrier is an important concern as well. In this paper, a simple method for ADC characterization is proposed based on the Phase Locked Loop (PLL) definition and on Phase and Amplitude Modulation (PM AM) measurements, focused in obtaining the relevant information of ADC noise contributions for phase noise measurement applications. The purpose of such a method is to find the parameters of a state ADC noise model using a technique which avoids the use of complex hardware and allows having a low computational costs performance. A digital calibration method is presented for delta-sigma ADCs with focus on feedback DAC mismatch error correction. The DAC mismatch information is acquired, and then the nonlinearity is cancelled in the digital domain while the ADC operates. Unlike dynamic element matching which is commonly run between non-overlapping clock phases, the proposed method does not have the restriction and introduces no excess loop delay. The effectiveness of the proposed scheme was demonstrated with implementation of a third-order 15-level delta-sigma ADC. A skip and fill algorithm is developed to digitally self-calibrate pipelined analog-to-digital converters (ADC's) in real time. The proposed digital calibration technique is applicable to capacitor-ratioed multiplying digital-to-analog converters (MDACs) commonly used in multistep or pipelined ADCs. This background calibration process can replace, in effect, a trimming procedure usually done in the factory with a hidden electronic calibration. Unlike other self-calibration techniques working in the foreground, the proposed technique is based on the concept of skipping conversion cycles randomly but filling in data later by nonlinear interpolation. This opens up the feasibility of digitally implementing calibration hardware and simplifying the task of self-calibrating multistep or pipelined ADCs. The proposed method improves the performance of the inherently fast ADCs by maintaining simple system architectures. To measure errors resulting from capacitor mismatch, of amp DC gain, offset, and switch feedthrough in real time, the calibration test signal is injected in place of the input signal using a split-reference injection technique. Ultimately, the missing signal within two-thirds of the Nyquist bandwidth is recovered with 16-b accuracy using a forty-fourth order polynomial interpolation, behaving essentially as an FIR filter,.