Abstract: Described is an apparatus which comprises: a first electrical path comprising at least one driver and receiver; and a second electrical path comprising at least one driver and receiver, wherein the first and second electrical paths are to receive a same input signal, wherein the first electrical path and the second electrical path are parallel to one another and have substantially the same propagation delays, and wherein the second electrical path is enabled during a first operation mode and disabled during a second operation mode.

Abstract: A full-rail digital-read CIM circuit enables a weighted read operation on a single row of a memory array. A weighted read operation captures a value of a weight stored in the single memory array row without having to rely on weighted row-access. Rather, using full-rail access and a weighted sampling capacitance network, the CIM circuit enables the weighted read operation even under process variation, noise and mismatch.

Abstract: A compute-in-memory (CIM) circuit that enables a multiply-accumulate (MAC) operation based on a current-sensing readout technique. An operational amplifier coupled with a bitline of a column of bitcells included in a memory array of the CIM circuit to cause the bitcells to act like ideal current sources for use in determining an analog voltage value outputted from the operational amplifier for given states stored in the bitcells and for given input activations for the bitcells. The analog voltage value sensed by processing circuitry of the CIM circuit and converted to a digital value to compute a multiply-accumulate (MAC) value.

Abstract: A binary CIM circuit enables all memory cells in a memory array to be effectively accessible simultaneously for computation using fixed pulse widths on the wordlines and equal capacitance on the bitlines. The fixed pulse widths and equal capacitance ensure that a minimum voltage drop in the bitline represents one least significant bit (LSB) so that the bitline voltage swing remains safely within the maximum allowable range. The binary CIM circuit maximizes the effective memory bandwidth of a memory array for a given maximum voltage range of bitline voltage.

Abstract: One embodiment provides a four stable state neuron. The four stable state neuron includes a plurality of input elements and a plurality of coupling channels. Each input element is coupled to a respective coupling channel and each input element is to scale a respective two-dimensional input signal by a weight. The four stable state neuron further includes a first output element coupled to the plurality of coupling channels. The first output element is to receive the plurality of weighted two-dimensional input signals and to generate a two-dimensional output signal based, at least in part, on a threshold value.

Abstract: Fermi filter field effect transistors having a Fermi filter between a source and a source contact, systems incorporating such transistors, and methods for forming them are discussed. Such transistors may include a channel between a source and a drain both having a first polarity and a Fermi filter between the source and a source contact such that the Fermi filter has a second polarity complementary to the first polarity.

Abstract: Embodiments of the present disclosure describe a semiconductor device having sub regions or distances to define threshold voltages. A first semiconductor device includes a first gate stack having a first edge opposing a second edge and a first source region disposed on the semiconductor substrate. A second semiconductor device includes a second gate stack having a third edge opposing a fourth edge and a second source region disposed on the semiconductor substrate. A first distance extends from the first source region to the first edge of the first gate stack and a second distance different from the first distance extends from the second source region to the third edge of the second gate stack.

Abstract: A memory device that includes a plurality subarrays of memory cells to store static weights and a plurality of digital full-adder circuits between subarrays of memory cells is provided. The digital full-adder circuit in the memory device eliminates the need to move data from a memory device to a processor to perform machine learning calculations. Rows of full-adder circuits are distributed between sub-arrays of memory cells to increase the effective memory bandwidth and reduce the time to perform matrix-vector multiplications in the memory device by performing bit-serial dot-product primitives in the form of accumulating m 1-bit x n-bit multiplications.

Abstract: Techniques and mechanisms for performing in-memory computations with circuitry having a pipeline architecture. In an embodiment, various stages of a pipeline each include a respective input interface and a respective output interface, distinct from said input interface, to couple to different respective circuitry. These stages each further include a respective array of memory cells and circuitry to perform operations based on data stored by said array. A result of one such in-memory computation may be communicated from one pipeline stage to a respective next pipeline stage for use in further in-memory computations. Control circuitry, interconnect circuitry, configuration circuitry or other logic of the pipeline precludes operation of the pipeline as a monolithic, general-purpose memory device. In other embodiments, stages of the pipeline each provide a different respective layer of a neural network.

Abstract: The present disclosure is directed to systems and methods of implementing a neural network using in-memory mathematical operations performed by pipelined SRAM architecture (PISA) circuitry disposed in on-chip processor memory circuitry. A high-level compiler may be provided to compile data representative of a multi-layer neural network model and one or more neural network data inputs from a first high-level programming language to an intermediate domain-specific language (DSL). A low-level compiler may be provided to compile the representative data from the intermediate DSL to multiple instruction sets in accordance with an instruction set architecture (ISA), such that each of the multiple instruction sets corresponds to a single respective layer of the multi-layer neural network model. Each of the multiple instruction sets may be assigned to a respective SRAM array of the PISA circuitry for in-memory execution.

Abstract: The present disclosure is directed to systems and methods of implementing a neural network using in-memory, bit-serial, mathematical operations performed by a pipelined SRAM architecture (bit-serial PISA) circuitry disposed in on-chip processor memory circuitry. The on-chip processor memory circuitry may include processor last level cache (LLC) circuitry. The bit-serial PISA circuitry is coupled to PISA memory circuitry via a relatively high-bandwidth connection to beneficially facilitate the storage and retrieval of layer weights by the bit-serial PISA circuitry during execution. Direct memory access (DMA) circuitry transfers the neural network model and input data from system memory to the bit-serial PISA memory and also transfers output data from the PISA memory circuitry to system memory circuitry.

Abstract: Techniques and mechanisms for configuring a memory device to perform a sequence of in-memory computations. In an embodiment, a memory device includes a memory array and circuitry, coupled thereto, to perform data computations based on the data stored at the memory array. Based on instructions received at the memory device, control circuitry is configured to enable an automatic performance of a sequence of operations. In another embodiment, the memory device is coupled in an in-series arrangement of other memory devices to provide a pipeline circuit architecture. The memory devices each function as a respective stage of the pipeline circuit architecture, where the stages each perform respective in-memory computations. Some or all such stages each provide a different respective layer of a neural network.

Abstract: The present disclosure is directed to systems and methods of implementing an analog neural network using a pipelined SRAM architecture (“PISA”) circuitry disposed in on-chip processor memory circuitry. The on-chip processor memory circuitry may include processor last level cache (LLC) circuitry. One or more physical parameters, such as a stored charge or voltage, may be used to permit the generation of an in-memory analog output using a SRAM array. The generation of an in-memory analog output using only word-line and bit-line capabilities beneficially increases the computational density of the PISA circuit without increasing power requirements.

Abstract: The present disclosure is directed to systems and methods of bit-serial, in-memory, execution of at least an nth layer of a multi-layer neural network in a first on-chip processor memory circuitry portion contemporaneous with prefetching and storing layer weights associated with the (n+1)st layer of the multi-layer neural network in a second on-chip processor memory circuitry portion. The storage of layer weights in on-chip processor memory circuitry beneficially decreases the time required to transfer the layer weights upon execution of the (n+1)st layer of the multi-layer neural network by the first on-chip processor memory circuitry portion. In addition, the on-chip processor memory circuitry may include a third on-chip processor memory circuitry portion used to store intermediate and/or final input/output values associated with one or more layers included in the multi-layer neural network.

Abstract: Compute-in memory circuits and techniques are described. In one example, a memory device includes an array of memory cells, the array including multiple sub-arrays. Each of the sub-arrays receives a different voltage. The memory device also includes capacitors coupled with conductive access lines of each of the multiple sub-arrays and circuitry coupled with the capacitors, to share charge between the capacitors in response to a signal. In one example, computing device, such as a machine learning accelerator, includes a first memory array and a second memory array. The computing device also includes an analog processor circuit coupled with the first and second memory arrays to receive first analog input voltages from the first memory array and second analog input voltages from the second memory array and perform one or more operations on the first and second analog input voltages, and output an analog output voltage.

Abstract: Embodiments include apparatuses, methods, and systems associated with save-restore circuitry including metal-ferroelectric-metal (MFM) devices. The save-restore circuitry may be coupled to a bit node and/or bit bar node of a pair of cross-coupled inverters to save the state of the bit node and/or bit bar node when an associated circuit block transitions to a sleep state, and restore the state of the bit node and/or bit bar node when the associated circuit block transitions from the sleep state to an active state. The save-restore circuitry may be used in a flip-flop circuit, a register file circuit, and/or another suitable type of circuit. The save-restore circuitry may include a transmission gate coupled between the bit node (or bit bar node) and an internal node, and an MFM device coupled between the internal node and a plate line. Other embodiments may be described and claimed.

Abstract: Techniques and mechanisms for a memory device to perform in-memory computing based on a logic state which is detected with a voltage-controlled oscillator (VCO). In an embodiment, a VCO circuit of the memory device receives from a memory array a first signal indicating a logic state that is based on one or more currently stored data bits. The VCO provides a conversion from the logic state being indicated by a voltage characteristic of the first signal to the logic state being indicated by a corresponding frequency characteristic of a cyclical signal. Based on the frequency characteristic, the logic state is identified and communicated for use in an in-memory computation at the memory device. In another embodiment, a result of the in-memory computation is written back to the memory array.

Abstract: A memory circuit has compute-in-memory circuitry that enables a multiply-accumulate (MAC) operation based on shared charge. Row access circuitry drives multiple rows of a memory array to multiply a first data word with a second data word stored in the memory array. The row access circuitry drives the multiple rows based on the bit pattern of the first data word. Column access circuitry drives a column of the memory array when the rows are driven. Accessed rows discharge the column line in an accumulative fashion. Sensing circuitry can sense voltage on the column line. A processor in the memory circuit computes a MAC value based on the voltage sensed on the column.

Abstract: An apparatus is described. The apparatus includes a compute in memory circuit. The compute in memory circuit includes a memory circuit and an encoder. The memory circuit is to provide 2m voltage levels on a read data line where m is greater than 1. The memory circuit includes storage cells sufficient to store a number of bits n where n is greater than m. The encoder is to receive an m bit input and convert the m bit input into an n bit word that is to be stored in the memory circuit, where, the m bit to n bit encoding performed by the encoder creates greater separation between those of the voltage levels that demonstrate wider voltage distributions on the read data line than others of the voltage levels.

Abstract: A semiconductor chip is described. The semiconductor chip includes a compute-in-memory (CIM) circuit to implement a neural network in hardware. The semiconductor chip also includes at least one output that presents samples of voltages generated at a node of the CIM circuit in response to a range of neural network input values applied to the CIM circuit to optimize the CIM circuit for the neural network.