Abstract: Methods and apparatus for performing machine learning tasks, and in particular, to a neural-network-processing architecture and circuits for improved handling of partial accumulation results in weight-stationary operations, such as operations occurring in compute-in-memory (CIM) processing elements (PEs). One example PE circuit for machine learning generally includes an accumulator circuit, a flip-flop array having an input coupled to an output of the accumulator circuit, a write register, and a first multiplexer having a first input coupled to an output of the write register, having a second input coupled to an output of the flip-flop array, and having an output coupled to a first input of the first accumulator circuit.

Abstract: Disclosed are packages that may include first and second substrates with first and second chips therebetween. The first chip may be a logic chip and the second chip may be a processing near memory (PNM) chip. The active side of the first chip may face the first substrate and the active side of the second chip may face the second substrate. The first chip may be encapsulated by a first mold, and the second chip may be encapsulated by a second mold. The first and/or the second molds may be thermally conductive. A third chip (e.g., a memory) may be on the second substrate opposite the second chip. The second substrate may include very short vertical connections that connect the active sides of the second and third chips.

Abstract: An integrated circuit (IC) is described. The IC includes a first die having a first semiconductor layer, a first active device layer and a first back-end-of-line (BEOL) layer. The IC also includes a second die having a second semiconductor layer, a second active device layer and a second back-end-of-line (BEOL) layer, and on the first die. The IC further includes a through substrate via (TSV) extending through the first die and the second die.

Abstract: A stacked system-on-chip (SoC) is described. The stacked SoC includes a first memory die comprising a dynamic random-access memory (DRAM). The stacked SoC also includes a compute logic die. The compute logic die comprises a static random-access memory (SRAM) having a first SRAM partition and a second SRAM partition. The first memory die is stacked on the compute logic die. The compute logic die includes a memory controller. The memory controller is coupled between the first SRAM partition and the second SRAM partition. Additionally, the memory controller is coupled to a DRAM bus of the first memory die.

Abstract: An integrated circuit includes a trench power rail to reduce resistance in a power rail or avoid an increase in resistance of a power rail as a result of the metal tracks being reduced in size as the technology node size is reduced. The trench power rail is formed in isolation regions between cell circuits. A cell isolation trench in the isolation region provides additional volume in which to dispose additional metal material for forming the trench power rail to increase its cross-sectional area. The trench power rail extends through a via layer to a metal layer, including signal interconnects. The trench power rail extends in a width direction out of the cell isolation trench in the via layer to couple to trench contacts of the adjacent cell circuits without vertical interconnect accesses (vias). A high-K dielectric layer can selectively isolate the trench power rail from the cell circuits.

Abstract: A 3D IC package includes a first package die having a first side coupled to a package substrate and a second side coupled to a second package die. The first package die includes vertical interconnects to provide interconnections between the second package die and the package substrate. The vertical interconnects each extend vertically between a first die contact on the first side of the first package die and a second die contact on the second side of the first package die. The second package die couples to the second die contacts of the first package die to form power and/or signal interconnects between the package substrate and the second package die. Horizontal interconnects in a distribution layer on the first side of the first package die distribute power and signals horizontally between the first die contacts and the vertical interconnects.

Abstract: Certain aspects generally relate to performing machine learning tasks, and in particular, to computation-in-memory architectures and operations. One aspect provides a circuit for in-memory computation. The circuit generally includes multiple bit-lines, multiple word-lines, an array of compute-in-memory cells, and a plurality of accumulators, each accumulator being coupled to a respective one of the multiple bit-lines. Each compute-in-memory cell is coupled to one of the bit-lines and to one of the word-lines and is configured to store a weight bit of a neural network.

Abstract: Disclosed are integrated circuit structures with buried rails and backside metals for routing input signals to and/or output signals from one or more cells of the integrated circuit structures. Port landing-free connections to input ports and/or from output ports are enabled. As a result, signal routing flexibility is enhanced.

Abstract: An integrated circuit includes a trench power rail to reduce resistance in a power rail or avoid an increase in resistance of a power rail as a result of the metal tracks being reduced in size as the technology node size is reduced. The trench power rail is formed in isolation regions between cell circuits. A cell isolation trench in the isolation region provides additional volume in which to dispose additional metal material for forming the trench power rail to increase its cross-sectional area. The trench power rail extends through a via layer to a metal layer, including signal interconnects. The trench power rail extends in a width direction out of the cell isolation trench in the via layer to couple to trench contacts of the adjacent cell circuits without vertical interconnect accesses (vias). A high-K dielectric layer can selectively isolate the trench power rail from the cell circuits.

Abstract: Certain aspects provide an apparatus for performing machine learning tasks, and in particular, to computation-in-memory architectures. One aspect provides a method for in-memory computation. The method generally includes: accumulating, via each digital counter of a plurality of digital counters, output signals on a respective column of multiple columns of a memory, wherein a plurality of memory cells are on each of the multiple columns, the plurality of memory cells storing multiple bits representing weights of a neural network, wherein the plurality of memory cells of each of the multiple columns correspond to different word-lines of the memory; adding, via an adder circuit, output signals of the plurality of digital counters; and accumulating, via an accumulator, output signals of the adder circuit.

Abstract: Methods and apparatus for performing machine learning tasks, and in particular, to a neural-network-processing architecture and circuits for improved performance through depth parallelism. One example neural-network-processing circuit generally includes a plurality of groups of processing element (PE) circuits, wherein each group of PE circuits comprises a plurality of PE circuits configured to process in parallel an input at a plurality of depths.

Abstract: Methods and apparatus for performing machine learning tasks, and in particular, to a neural-network-processing architecture and circuits for improved handling of partial accumulation results in weight-stationary operations, such as operations occurring in compute-in-memory (CIM) processing elements (PEs). One example PE circuit for machine learning generally includes an accumulator circuit, a flip-flop array having an input coupled to an output of the accumulator circuit, a write register, and a first multiplexer having a first input coupled to an output of the write register, having a second input coupled to an output of the flip-flop array, and having an output coupled to a first input of the first accumulator circuit.

Abstract: Digital compute-in-memory (DCIM) bit cell circuit layouts and DCIM array circuits for multiple operations per column are disclosed. A DCIM bit cell array circuit including DCIM bit cell circuits comprising exemplary DCIM bit cell circuit layouts disposed in columns is configured to evaluate the results of multiple multiply operations per clock cycle. The DCIM bit cell circuits in the DCIM bit cell circuit layouts each couples to one of a plurality of column output lines in a column. In this regard, in each cycle of a system clock, each of the plurality of column output lines receives a result of a multiply operation of a DCIM bit cell circuit coupled to the column output line. The DCIM bit cell array circuit includes digital sense amplifiers coupled to each of the plurality of column output lines to reliably evaluate a result of a plurality of multiply operations per cycle.

Abstract: Certain aspects provide an apparatus for performing machine learning tasks, and in particular, to computation-in-memory architectures. One aspect provides a circuit for in-memory computation. The circuit generally includes: a plurality of memory cells on each of multiple columns of a memory, the plurality of memory cells being configured to store multiple bits representing weights of a neural network, wherein the plurality of memory cells on each of the multiple columns are on different word-lines of the memory; multiple addition circuits, each coupled to a respective one of the multiple columns; a first adder circuit coupled to outputs of at least two of the multiple addition circuits; and an accumulator coupled to an output of the first adder circuit.

Abstract: Certain aspects generally relate to performing machine learning tasks, and in particular, to computation-in-memory architectures and operations. One aspect provides a circuit for in-memory computation. The circuit generally includes multiple bit-lines, multiple word-lines, an array of compute-in-memory cells, and a plurality of accumulators, each accumulator being coupled to a respective one of the multiple bit-lines. Each compute-in-memory cell is coupled to one of the bit-lines and to one of the word-lines and is configured to store a weight bit of a neural network.

Abstract: Methods and apparatus for performing machine learning tasks, and in particular, a hybrid architecture that includes both neural processing unit (NPU) and compute-in-memory (CIM) elements. One example neural-network-processing circuit generally includes a plurality of CIM processing elements (PEs), a plurality of neural processing unit (NPU) PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs. One example method for neural network processing generally includes processing data in a neural-network-processing circuit comprising a plurality of CIM PEs, a plurality of NPU PEs, and a bus coupled to the plurality of CIM PEs and to the plurality of NPU PEs; and transferring the processed data between at least one of the plurality of CIM PEs and at least one of the plurality of NPU PEs via the bus.

Abstract: Digital compute-in-memory (DCIM) bit cell circuit layouts and DCIM array circuits for multiple operations per column are disclosed. A DCIM bit cell array circuit including DCIM bit cell circuits comprising exemplary DCIM bit cell circuit layouts disposed in columns is configured to evaluate the results of multiple multiply operations per clock cycle. The DCIM bit cell circuits in the DCIM bit cell circuit layouts each couples to one of a plurality of column output lines in a column. In this regard, in each cycle of a system clock, each of the plurality of column output lines receives a result of a multiply operation of a DCIM bit cell circuit coupled to the column output line. The DCIM bit cell array circuit includes digital sense amplifiers coupled to each of the plurality of column output lines to reliably evaluate a result of a plurality of multiply operations per cycle.

Abstract: Aspects of the disclosure are directed to an integrated circuit. The integrated circuit may include a signaling interconnect having a narrow trench disposed within a metallization layer, and a power rail having a wide trench disposed within the metallization layer, wherein the signaling interconnect comprises non-copper material and the power rail comprises copper. The non-copper material may include at least one of ruthenium (Ru), tungsten (W), aluminum (Al), and cobalt (Co). The signaling interconnect and power rail may be processed in a common chemical mechanical polishing step and have approximately the same trench depth. A metal cap may be deposited on top of the power rail.

Abstract: The disclosed technology generally relates to semiconductor devices and more particularly to a gate structure for a semiconductor device, and to methods of forming the same. In an aspect a method for forming a gate structure includes forming a first set of one or more semiconductor features and a second set of one or more semiconductor features. The method additionally includes forming a sacrificial gate extending across the semiconductor features of the first set and the semiconductor features of the second set.

Abstract: Integrated circuits employing varied gate topography between an active gate region(s) and a field gate region(s) in a gate(s) for reduced gate layout parasitic capacitance, and related methods, are disclosed. In exemplary aspects, the gate topography (e.g., height) of a gate in a circuit cell used to form gates for devices formed therein to form an integrated circuit is varied between an active gate and a field gate(s) of the gate. In this manner, the overall volume of material in the gate can be reduced due to the reduction in volume of the field gate(s) to reduce gate layout parasitic capacitance. Reducing gate layout parasitic capacitance in a circuit cell can reduce the overall parasitic capacitance of an integrated circuit formed from the circuit cell to achieve the desired integrated circuit delay performance.