Abstract: Methods for performing mixed-mode Multiply-Accumulate (MAC) functions in an integrated circuit (IC) are disclosed. By performing part of the MAC operation spatially and in parallel, and part of it temporally and serially, the number of MAC operations can be programmed in the serial/temporal MAC segment as a multiple of the parallel/spatial MAC segment. Such a trait provides a degree of flexibility in programming the mixed-mode MAC function. A Programmable-Hybrid-Accumulation (PHA) method, performs the accumulation function of the MAC IC, by transforming the accumulation signal to a hybrid accumulation signal. The hybrid accumulation signal is comprised of a Most-Significant-Portion (MSP) and a Least-Significant-Portion (LSP), wherein the portions of the hybrid accumulation signal can be programmed in accordance with cost-performance objectives of an end application.

Abstract: Methods of performing mixed-signal/analog multiply-accumulate (MAC) operations used for matrix multiplication in fully connected artificial neural networks in integrated circuits (IC) are described in this disclosure having traits such as: (1) inherently fast and efficient for approximate computing due to current-mode signal processing where summation is performed by simply coupling wires, (2) free from noisy and power hungry clocks with asynchronous fully-connected operations, (3) saving on silicon area and power consumption for requiring neither any data-converters nor any memory for intermediate activation signals, (4) reduced dynamic power consumption due to Compute-In-Memory operations, (5) avoiding over-flow conditions along key signals paths and lowering power consumption by training MACs in neural networks in such a manner that the population and or combinations of multi-quadrant activation signals and multi-quadrant weight signals follow a programmable statistical distribution profile, (6) programmab

Abstract: Digital approximate multipliers (aMULT) utilizing interpolative apparatuses, circuits, and methods are described in this disclosure. The disclosed aMULT interpolative methods can be arranged or programmed to operate asynchronously and or synchronously. For applications where less precision is acceptable, fewer interpolations can yield less precise multiplication results, while such approximate multiplication can be computed faster and at lower power consumption. Conversely, for applications where higher precision is required, more interpolations can generate more precise multiplication results.

Abstract: Analog multipliers can perform signal processing with approximate precision asynchronously (clock free) and with low power consumptions, which can be advantageous including in emerging mobile and portable artificial intelligence (AI) and machine learning (ML) applications near or at the edge and or near sensors. Based on low cost, mainstream, and purely digital Complementary-Metal-Oxide-Semiconductor (CMOS) manufacturing process, the present invention discloses embodiments of current-mode analog multipliers that can be utilized in multiply-accumulate (MAC) signal processing in end-application that require low cost, low power consumption, (clock free) and asynchronous operations.

Abstract: Digital approximate squarer (aSQR)s utilizing apparatuses, circuits, and methods are described in this disclosure. The disclosed aSQR methods can operate asynchronously and or synchronously. For applications where low precisions is acceptable, fewer interpolations can yield less precise square approximation, which can be computed faster and with lower power consumption. Conversely, for applications where higher precision are required, more interpolations steps can generate more precise square approximation. By utilizing the disclosed aSQR method, precision objectives of a squarer approximation function can be programmed real-time and on the fly, which enables optimizing for power consumption and speed of squaring, in addition to optimize for the approximate squarer's die size and cost.

Abstract: Analog multipliers can perform signal processing with approximate precision asynchronously (clock free) and with low power consumptions, which can be advantageous including in emerging mobile and portable artificial intelligence (AI) and machine learning (ML) applications near or at the edge and or near sensors. Based on low cost, mainstream, and purely digital Complementary-Metal-Oxide-Semiconductor (CMOS) manufacturing process, the present invention discloses embodiments of current-mode analog multipliers that can be utilized in multiply-accumulate (MAC) signal processing in end-application that require low cost, low power consumption, (clock free) and asynchronous operations.

Abstract: Multipliers and Multiply-Accumulate (MAC) circuits are fundamental building blocks in signal processing, including in emerging applications such as machine learning (ML) and artificial intelligence (AI) that predominantly utilize digital-mode multipliers, and MACs. Typically, digital multipliers and MACs can operate at high speed with high resolution, and synchronously. As the resolution and speed of digital multipliers, and MACs increase, usually the dynamic power consumption and chip size of digital implementations increases substantially that makes them impractical for some ML and AI segments, including in portable, mobile, near edge, or near sensor applications.

Abstract: Multipliers and Multiply-Accumulate (MAC) circuits are fundamental building blocks in signal processing, including in emerging applications such as machine learning (ML) and artificial intelligence (AI) that predominantly utilize digital-mode multipliers and MACs. Generally, digital multipliers and MACs can operate at high speed with high resolution, and synchronously. As the resolution and speed of digital multipliers and MACs increase, generally the dynamic power consumption and chip size of digital implementations increases substantially that makes them impractical for some ML and AI segments, including in portable, mobile, near edge, or near sensor applications.

Abstract: Methods of performing mixed-signal current-mode multiply-accumulate (MAC) operations for binarized neural networks in an integrated circuit are described in this disclosure. While digital machine learning circuits are fast, scalable, and programmable, they typically require bleeding-edge deep sub-micron manufacturing, consume high currents, and they reside in the cloud, which can exhibit long latency, and not meet private and safety requirements of some applications. Digital machine learning circuits also tend to be pricy given that machine learning digital chips typically require expensive tooling and wafer fabrication associated with advanced bleeding-edge deep sub-micron semiconductor manufacturing.

Abstract: Multipliers, Multiply-Accumulate (MAC), and Square-Accumulate (SAC) circuits are fundamental building blocks in signal processing, including in emerging applications such as machine learning (ML) and artificial intelligence (AI) that predominantly utilize digital-mode multipliers, MACs, and SACs. Generally, digital multipliers, MACs, and SACs can operate at high speed with high resolution, and synchronously. As the resolution and speed of digital multipliers, MACs, and SACs increase, generally the dynamic power consumption and chip size of digital implementations increases substantially that makes them impractical for some ML and AI segments, including in portable, mobile, near edge, or near sensor applications.

Abstract: Multipliers and Multiply-Accumulate (MAC) circuits are fundamental building blocks in signal processing, including in emerging applications such as machine learning (ML) and artificial intelligence (AI) that predominantly utilize digital-mode multipliers and MACs. Generally, digital multipliers and MACs can operate at high speed with high resolution, and synchronously. As the resolution and speed of digital multipliers and MACs increase, generally the dynamic power consumption and chip size of digital implementations increases substantially that makes them impractical for some ML and AI segments, including in portable, mobile, near edge, or near sensor applications.

Abstract: Single-stage and multiple-stage current-mode Analog-to-Digital converters (iADC)s utilizing apparatuses, circuits, and methods are described in this disclosure. The disclosed iADCs can operate asynchronously and be free from the digital clock noise, which also lowers dynamic power consumption, and reduces circuitry overhead associated with free running clocks. For their pseudo-flash operations, the disclosed iADCs do not require their input current signals to be replicated which saves area, lowers power consumption, and improves accuracy. Moreover, the disclosed methods of multi-staging of iADCs increase their resolutions while keeping current consumption and die size (cost) low. The iADC's asynchronous topology facilitates decoupling analog-computations from digital-computations, which helps reduce glitch, and facilitates gradual degradation (instead of an abrupt drop) of iADC's accuracy with increased input current signal frequency. The iADCs can be arranged with minimal digital circuitry (i.e.

Abstract: Multipliers and Multiply-Accumulate (MAC) circuits are fundamental building blocks in signal processing, including in emerging applications such as machine learning (ML) and artificial intelligence (AI) that predominantly utilize digital-mode multipliers and MACs. Generally, digital multipliers and MACs can operate at high speed with high resolution, and synchronously. As the resolution and speed of digital multipliers and MACs increase, generally the dynamic power consumption and chip size of digital implementations increases substantially that makes them impractical for some ML and AI segments, including in portable, mobile, near edge, or near sensor applications.

Abstract: Single-stage and multiple-stage current-mode Analog-to-Digital converters (iADC)s utilizing apparatuses, circuits, and methods are described in this disclosure. The disclosed iADCs can operate asynchronously and be free from the digital clock noise, which also lowers dynamic power consumption, and reduces circuitry overhead associated with free running clocks. For their pseudo-flash operations, the disclosed iADCs do not require their input current signals to be replicated which saves area, lowers power consumption, and improves accuracy. Moreover, the disclosed methods of multi-staging of iADCs increase their resolutions while keeping current consumption and die size (cost) low. The iADC's asynchronous topology facilitates decoupling analog-computations from digital-computations, which helps reduce glitch, and facilitates gradual degradation (instead of an abrupt drop) of iADC's accuracy with increased input current signal frequency. The iADCs can be arranged with minimal digital circuitry (i.e.

Abstract: Analog multipliers circuits can provide signal processing asynchronously and clock free and with low power consumptions, which can be advantageous, including in emerging mobile, portable, and at edge or near sensor artificial intelligence (AI) and machine learning (ML) applications. As such, analog multipliers can process signals memory-free in AI and ML applications, which avoids the power consumption and latency delays attributed to memory read-write cycles in conventional AI and ML digital processors. Based on standard digital Complementary-Metal-Oxide-Semiconductor (CMOS) manufacturing process, the present invention discloses embodiments of multi-quadrant current-mode analog multiplier (iMULT) circuits that can be utilized in current-mode multiply-accumulate (iMAC) circuits and artificial neural network (ANN) end-applications that require high-volumes, low costs, medium precision, low power consumptions, and clock free asynchronous signal processing.

Abstract: Multipliers and Multiply-Accumulate (MAC) circuits are fundamental building blocks in signal processing, including in emerging applications such as machine learning (ML) and artificial intelligence (AI) that predominantly utilize digital-mode multipliers and MACs. Generally, digital multipliers and MACs can operate at high speed with high resolution, and synchronously. As the resolution and speed of digital multipliers and MACs increase, generally the dynamic power consumption and chip size of digital implementations increases substantially that makes them impractical for some ML and AI segments, including in portable, mobile, near edge, or near sensor applications.

Abstract: Analog multipliers can perform signal processing with approximate precision asynchronously (clock free) and with low power consumptions, which can be advantageous including in emerging mobile and portable artificial intelligence (AI) and machine learning (ML) applications near or at the edge and or near sensors. Based on low cost, mainstream, and purely digital Complementary-Metal-Oxide-Semiconductor (CMOS) manufacturing process, the present invention discloses embodiments of current-mode analog multipliers that can be utilized in multiply-accumulate (MAC) signal processing in end-application that require low cost, low power consumption, (clock free) and asynchronous operations.

Abstract: A family of current mode analog to digital converters, or TiADC, utilizing methods, circuits, and apparatuses, are disclosed with the following benefits: (1) There are normal and random non-systematic mismatch between devices in silicon manufacturing, that introduce non-linearity in current mode analog to digital converter's, or iADC, reference network. The iADC's linearity is improved by utilizing a thermometer current mode signal conditioning method, SCM. Successive applications of the SCM effectuates a segmented current reference network to function like a thermometer network, which operates based on the function of summation. Having a TiADC with a thermometer reference network, where current segments are summed or accumulated incrementally, would inherently reduce the impact of statistical distribution of component's random mismatch on the iADC's non-linearity.

Abstract: Multipliers and Multiply-Accumulate (MAC) circuits are fundamental building blocks in signal processing, including in emerging applications such as machine learning (ML) and artificial intelligence (AI) that predominantly utilize digital-mode multipliers and MACs. Generally, digital multipliers and MACs can operate at high speed with high resolution, and synchronously. As the resolution and speed of digital multipliers and MACs increase, generally the dynamic power consumption and chip size of digital implementations increases substantially that makes them impractical for some ML and AI segments, including in portable, mobile, near edge, or near sensor applications.

Abstract: Single-stage and multiple-stage current-mode Analog-to-Digital converters (iADC)s utilizing apparatuses, circuits, and methods are described in this disclosure. The disclosed iADCs can operate asynchronously and be free from the digital clock noise, which also lowers dynamic power consumption, and reduces circuitry overhead associated with free running clocks. For their pseudo-flash operations, the disclosed iADCs do not require their input current signals to be replicated which saves area, lowers power consumption, and improves accuracy. Moreover, the disclosed methods of multi-staging of iADCs increase their resolutions while keeping current consumption and die size (cost) low. The iADC's asynchronous topology facilitates decoupling analog-computations from digital-computations, which helps reduce glitch, and facilitates gradual degradation (instead of an abrupt drop) of iADC's accuracy with increased input current signal frequency. The iADCs can be arranged with minimal digital circuitry (i.e.