Abstract: A radiofrequency frontend device includes a memory array, which includes a plurality of input lines; a plurality of output lines; and a plurality of impedance devices, each impedance device connecting an input line of the plurality of input lines to an output line of the plurality of output lines, wherein each impedance represents a filter coefficient; wherein the radiofrequency frontend device is configured to provide at each input line of the plurality of input lines a sampled voltage of an analog electric signal, each sampled voltage corresponding to a voltage of the analog electric signal during a respective time period of a plurality of time periods; and when the memory array receives the sampled voltages, the memory array is configured to modify each of the sampled voltages by a respective impedance device of the plurality of impedance devices and sum the modified sampled voltages.

Abstract: Systems, apparatuses and methods may provide for technology that includes a capacitor ladder, a plurality of memory cells coupled to the capacitor ladder, the plurality of memory cells to control the capacitor ladder to conduct multi-bit multiply accumulate (MAC) operations during a computation phase, and a successive approximation register (SAR) coupled to the capacitor ladder, the SAR to control the capacitor ladder to digitize results of the multi-bit MAC operations during a digitization phase.

Abstract: Systems, apparatuses and methods include technology that identifies workload numbers associated with a workload. The technology converts the workload numbers to block floating point numbers based on a division of mantissas of the workload numbers into sub-words and executes a compute-in memory operation based on the sub-words to generate partial products.

Abstract: Circuitry for a compute-in-memory (CiM) circuit or structure arranged to detect bit errors in a group of memory cells based on a summation of binary 1's included in at least one weight matrix stored to the group of memory cells, a parity value stored to another group of memory cells and a comparison of the summation or the parity value to an expected value.

Abstract: A compute-in-memory (CiM) circuit or structure arranged to detect errors. Examples include detecting errors associated with weight bits stored to computational nodes included in a CiM circuit or structure based on use of complimented bit values. Examples also include detecting errors in the CiM circuit or structure based on using at least some computational nodes included in an array of computational nodes to monitor for the errors during generation of computation results by other computational nodes included in the array.

Abstract: Embodiments herein relate to a neural network processor in a control loop, where the control loop sets an optimum supply voltage for the processor based on a measured error count or rate of the neural network. For example, if the measured error count is greater than a target level or range, the supply voltage can be increased. If the measured error count is below the target level or range, the supply voltage can be decreased. The error rate can be measured by providing an error detection circuit for one or more monitored nodes/processing units of a neural network. The error detection circuit can receive the same input data as the associated monitored processing unit, but operates on only a portion of the input data.

Abstract: Systems, apparatuses and methods may provide for technology that includes a memory array to store multibit weight data and a capacitor ladder network to conduct multiply-accumulate (MAC) operations on first analog signals and multibit weight data, the capacitor ladder network further to output second analog signals based on the MAC operations, wherein the capacitor ladder network is external to the memory array. In one example, the capacitor ladder network includes a plurality of switches and the logic includes a controller to selectively activate the plurality of switches based on a data format of the multibit weight data.

Abstract: Systems, apparatuses and methods may provide for technology that includes a plurality of analog to digital converters (ADCs), compute-in-memory (CiM) multiply-accumulate (MAC) hardware coupled to the plurality of ADCs, and a plurality of digital to analog converters (DACs) coupled to the CiM MAC hardware, wherein the plurality of DACs includes one or more redundant DACs. In one example, the technology uses a DAC disconnect scheme to statically bypass defective memory bitcells and compute capacitors to improve yield with minimal overhead, and dynamically boost the effective precision of the ADC in the presence of weight/activation sparsity in neural network (NN) compute.

Abstract: Systems, apparatuses and methods may provide for compute-in-memory (CiM) accelerator technology that includes a multiply-accumulate (MAC) computation stage, an analog amplifier stage coupled to an output of the MAC computation stage, and an analog to digital conversion (ADC) stage coupled to an output of the analog amplifier stage, wherein a gain setting of the analog amplifier stage modifies a quantization granularity of the ADC stage.

Abstract: A wireless communication device, including a radiofrequency frontend, configured to wirelessly receive a radiofrequency signal; perform one or more analog baseband operations on the received radiofrequency signal, according to a radio access technology; and output an analog signal representing an output of the analog baseband operations on the received radiofrequency signal; an error corrector, configured to perform an error correction operation on the analog signal; and output an error corrected signal in analog domain; and the analog-digital converter, configured to convert the error corrected signal to digital domain.

Abstract: Various embodiments provide apparatuses, systems, and methods for multibit analog representation, e.g., for in-memory computing. Embodiments may include a single-ended or differential ladder network to generate an analog value (e.g., a voltage or charge) based on a set of bits from a memory array. The ladder network may include a plurality of branches coupled to an output line, wherein individual branches include a capacitor with a first terminal coupled to the output line and a switch coupled to a second terminal of the capacitor. The switch may be controlled by a respective bit of the set of bits to selectively couple the second terminal of the capacitor to a first voltage node or a second voltage node based on a value of the respective bit. Other embodiments may be described and claimed.

Abstract: Technology for generating an SRAM-based in-memory computing macro includes replacing a SRAM cell cluster defined by a generic SRAM macro with a single-bit multi-bank cluster, the single-bit multi-bank cluster including a plurality of CiM SRAM cells and a plurality of C-2C capacitor ladder cells, arranging a plurality of single-bit multi-bank clusters to form a multi-bit multi-bank cluster, and arranging a plurality of multi-bit multi-bank clusters into a multi-dimensional MAC computational unit within a region of the generic SRAM macro, where an output of at least two of the multi-bit multi-bank clusters are electrically coupled to form an output analog activation line, and where a plurality of bit lines and a plurality of word lines remain at the same grid locations as provided in the generic SRAM macro. Embodiments include arranging a plurality of multi-dimensional MAC computational units into an in-memory MAC computing array.

Abstract: Systems, apparatuses, and methods include technology that identifies that a first memory cell of a plurality of memory cells stores data that is associated with a multiply-accumulate operation. The plurality of memory cells is associated with a multiply-accumulator (MAC). The technology executes a connection operation to electrically connect the first memory cell to the MAC to execute the multiply-accumulate operation. A second memory cell of the plurality of memory cells is electrically disconnected from the MAC during the multiply-accumulate operation. The technology executes, with the MAC, the multiply-accumulate operation based on the data.

Abstract: Dynamic compensation of analog circuitry impairments in ANNs is provided. An example ANN includes an analog circuitry that performs MAC operations based on weights. To compensate analog circuitry impairments, a signal package including a training signal and an input signal, is formed. The training signal is fed into the ANN. The ANN generates an output signal through MAC operations by the analog circuitry with the training signal and the weights. The output signal is compared with a reference signal to determine an error in the output signal. The reference signal may include one or more ground-truth classifications of the training signal. The error is used to compute a compensation coefficient, which compensates impact of analog circuitry impairments on accuracy in outputs of the ANN. The ANN is updated with the compensation coefficient. The analog circuitry performs MAC operations with the input signal, the compensation coefficient, and the set of weights.

Abstract: An analog neural network including a hardware activation function is provided. A layer of the analog neural network includes a sequence of processing elements that receives analog signals, perform MAC operations on the analog signals, and generates analog outputs. The analog outputs are provided to an analog circuitry that can apply an activation function on the analog outputs. The output of the analog circuitry are also analog signals, which can further be provided to the next layer in the network. The analog circuitry may include a differential pair of transistors to compute the tan h activation function. Alternatively, the analog circuitry may include a comparator and multiplexer to compute the ReLU activation function. Compared with digital implementation of activation functions, the analog circuitry eliminates the need of converting the analog outputs of the layer to digital signals and the need of converting the result of the activation function to analog signals.

Abstract: Systems, apparatuses and methods include technology that receives, with a first plurality of multipliers of a multiply-accumulator (MAC), first digital signals from a memory array, wherein the first plurality of multipliers includes a plurality of capacitors. The technology further executes, with the first plurality of multipliers, multibit computation operations with the plurality of capacitors based on the first digital signals, and generates, with the first plurality of multipliers, a first analog signal based on the multibit computation operations.

Abstract: The present disclosure includes a time-to-digital converter (TDC) based RF-to-digital (RDC) data converter for time domain signal processing polar receivers. Polar data conversion achieves better SNR tolerance owing to its phase convergence near the origin in a polar coordinate. The proposed RDC consists of a TDC for phase detection and an analog-to-digital converter (ADC) for amplitude conversion. Unlike the conversional data converter, the proposed ADC's sampling position is guided by the detected phase result from the TDC's output. This TDC assisted data-converter architecture reduces the number of bits required for the ADC. In addition, oversampling is no longer needed. With precisely controlled tunable delay cells and gain compensator, this hybrid data convertor is capable to directly convert Quadrature Amplitude Modulation (QAM) waveforms and Amplitude Phase Shift Keying (APSK) waveforms directly from the RF signal without down-conversion.

Abstract: The present disclosure includes a time-to-digital converter (TDC) based RF-to-digital (RDC) data converter for time domain signal processing polar receivers. Polar data conversion achieves better SNR tolerance owing to its phase convergence near the origin in a polar coordinate. The proposed RDC consists of a TDC for phase detection and an analog-to-digital converter (ADC) for amplitude conversion. Unlike the conversional data converter, the proposed ADC's sampling position is guided by the detected phase result from the TDC's output. This TDC assisted data-converter architecture reduces the number of bits required for the ADC. In addition, oversampling is no longer needed. With precisely controlled tunable delay cells and gain compensator, this hybrid data convertor is capable to directly convert Quadrature Amplitude Modulation (QAM) waveforms and Amplitude Phase Shift Keying (APSK) waveforms directly from the RF signal without down-conversion.

Abstract: Systems, methods, apparatuses, and computer-readable media. An analog router of a first supertile of a plurality of supertiles of a network on a chip (NoC) may receive a first analog output from a first compute-in-memory tile of a plurality of compute-in-memory tiles of the first supertile. The analog router may determine, based on a configuration of a neural network executing on the NoC, that a destination of the first analog output includes a second supertile of the plurality of supertiles. An analog-to-digital converter (ADC) of the analog router may convert the first analog output to a first digital output and transmit the first digital output to the second supertile via a communications bus of the NoC.

Abstract: A semiconductor package comprises a controlled voltage domain (CVD) and a master voltage domain (MVD). The MVD comprises an error-tolerance control (ETC) circuit. A basic execution block in the CVD generates a basic output value, based on at least two input values. A test execution block in the CVD generates a test digital root, based on digital roots of the input values. A digital root comparator in the CVD determines whether a digital root of the basic output value matches the test digital root. An error reporter in the CVD sends an error report to the ETC circuit in response to a determination that the digital roots do not match. The ETC may automatically adjust at least one power characteristic of the CVD, based on the error report. Other embodiments are described and claimed.