Abstract:
A graphics processing unit (GPU) includes a plurality of programmable processing cores configured to process graphics primitives and corresponding data and a plurality of fixed-function hardware units. The plurality of processing cores and the plurality of fixed-function hardware units are configured to implement a configurable number of virtual pipelines to concurrently process different command flows. Each virtual pipeline includes a configurable number of fragments and an operational state of each virtual pipeline is specified by a different context. The configurable number of virtual pipelines can be modified from a first number to a second number that is different than the first number. An emulation of a fixed-function hardware unit can be instantiated on one or more of the graphics processing cores in response to detection of a bottleneck in a fixed-function hardware unit. One or more of the virtual pipelines can then be reconfigured to utilize the emulation instead of the fixed-function hardware unit.
Abstract:
Disclosed methods, systems, and computer program products embodiments include synchronizing a group of workitems on a processor by storing a respective program counter associated with each of the workitems, selecting at least one first workitem from the group for execution, and executing the selected at least one first workitem on the processor. The selecting is based upon the respective stored program counter associated with the at least one first workitem.
Abstract:
The disclosed circuit can interpret a bit sequence as a value based on one of multiple floating point number formats in a bias mode indicated by a bias mode indicator. The circuit can and perform an operation using the value in the bias mode. Various other methods, systems, and computer-readable media are also disclosed.
Abstract:
A processing system includes one or more accelerator units (AUs) each having a modular architecture. To this end, each AU includes a connection circuitry and one or more memory stacks disposed on the connection circuitry. Further, each AU includes one or more interposer dies each disposed on the connection circuitry such that each interposer die of the one or more interposer dies is communicatively coupled to a corresponding memory stack of the memory stacks via the connection circuitry. Further, each interposer die of each AU includes circuitry configured to concurrently support two or more types of compute dies.
Abstract:
Methods and systems are provided for generating a stylized representation of a non-player character (NPC) in a virtual environment. A multimodal plurality of inputs regarding characteristics of the NPC is received, which is processed to generate visual data representing the NPC's appearance and to generate behavior data representing the NPC's actions. The generated visual data and behavior data are adapted to a selected character model to create an adapted configuration model, which is used to generate rendering information for the NPC.
Abstract:
A graphics processing unit (GPU) or other apparatus includes a plurality of shader engines. The apparatus also includes a first front end (FE) circuit and one or more second FE circuits. The first FE circuit is configured to schedule geometry workloads for the plurality of shader engines in a first mode. The first FE circuit is configured to schedule geometry workloads for a first subset of the plurality of shader engines and the one or more second FE circuits are configured to schedule geometry workloads for a second subset of the plurality of shader engines in a second mode. In some cases, a partition switch is configured to selectively connect the first FE circuit or the one or more second FE circuits to the second subset of the plurality of shader engines depending on whether the apparatus is in the first mode or the second mode.
Abstract:
Systems and techniques for generating and animating non-player characters (NPCs) within virtual digital environments are provided. Multimodal input data is received that comprises a plurality of input modalities for interaction with an NPC having a set of body features and a set of facial features. The multimodal input data is processed through one or more neural networks to generate animation sequences for both the body features and facial features of the NPC. Generating such animation sequences includes disentangling the multimodal input data to generate substantially disentangled latent representations, combining these representations with the multimodal input data, and using a large-language model (LLM) to generate speech data for the NPC. Further processing using reverse diffusion generates face vertex displacement data and joint trajectory data based on the combined representation and generated speech data. The face vertex displacement data, joint trajectory data, and speech data are used to produce an animated representation of the NPC, which is then provided to environment-specific adapters to animate the NPC within a virtual digital environment.
Abstract:
A processing system includes a first set and a second set of general-purpose registers (GPRs) and memory access circuitry that fetches nonzero values of a sparse matrix into consecutive slots in the first set. The memory access circuitry also fetches values of an expanded matrix into consecutive slots in the second set of GPRs. The expanded matrix is formed based on values of a vector and locations of the nonzero values in the sparse matrix. The processing system also includes a set of multipliers that concurrently perform multiplication of the nonzero values in slots of the first set of GPRs with the values of the vector in corresponding slots of the second set. Reduced sum circuitry accumulates results from the set of multipliers for rows of the sparse matrix.
Abstract:
Described herein is a technique for performing ray tracing operations. The technique includes encountering, at a non-leaf node, a pointer to a bottom-level acceleration structure having one or more delta instances; identifying an index associated with the pointer, wherein the index identifies an instance within the bottom-level acceleration structure; and obtaining data for the instance based on the pointer and the index.
Abstract:
An array processor includes processor element arrays distributed in rows and columns. The processor element arrays perform operations on parameter values. The array processor also includes memory interfaces that are dynamically mapped to mutually exclusive subsets of the rows and columns of the processor element arrays based on dimensions of matrices that provide the parameter values to the processor element arrays. In some cases, the processor element arrays are vector arithmetic logic unit (ALU) processors and the memory interfaces are direct memory access (DMA) engines. The rows of the processor element arrays in the subsets are mutually exclusive to the rows in the other subsets and the columns of the processor element arrays in the subsets are mutually exclusive to the columns in the other subsets. The matrices can be symmetric or asymmetric, e.g., one of the matrices can be a vector having a single column.