Abstract: Existing model deployment approaches have the disadvantage that they do not consider feasibility of cloud instances for hosting a given LLM model. Embodiments disclosed herein provide a method and system for deployment of LLMs in a plurality of cloud instances. The system checks feasibility of the plurality of cloud instances for hosting an LLM, based on size of the LLM and storage space in each of the cloud instances. Further, a latency value for a plurality of batch sizes is determined for a plurality of LLM-accelerator pairs, in each of the plurality of cloud instances identified as feasible based on the feasibility check, using a performance model. Furthermore, a recommendation of one of the plurality of cloud instances identified as feasible is generated, based on the determined latency, a measured cost of deployment, a user workload, an application type, a plurality of latency constraints, and an evaluated performance.

Abstract: Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.

Abstract: The disclosure generally relates to an FPGA-based online 3D bin packing. Online 3D bin packing is the process of packing boxes into larger bins-Long Distance Containers (LDCs) such that the space inside each LDC is used to the maximum extent. The use of deep reinforcement learning (Deep RL) for this process is effective and popular. However, since the existing processor-based implementations are limited by Von-Neumann architecture and take a long time to evaluate each alignment for a box, only a few potential alignments are considered, resulting in sub-optimal packing efficiency. This disclosure describes an architecture for bin packing which leverages pipelining and parallel processing on FPGA for faster and exhaustive evaluation of all alignments for each box resulting in increased efficiency. In addition, a suitable generic purpose processor is employed to train the neural network within the algorithm to make the disclosed techniques computationally light, faster and efficient.

Abstract: High-performance deployment of DNN recommendation models heavily rely on embedding tables, and their performance bottleneck lies in the latency of embedding access. To optimize the deployment of RMs, the method and system is disclosed, which leverages heterogeneous memory types on FPGAs to improve the overall performance by maximizing the availability of frequently accessed data in faster memory. The system, using a optimizer dynamically allocates table partitions of the embedding tables based on history of input access history. A pre-optimizer block disclosed determines whether smaller tables should be partitioned or placed entirely in smaller memories, improving overall efficiency. The performance of RM is improved with improvement in average embedding fetch latency and effectively inference latency via modified Round Trip computation.

Abstract: Existing techniques fail to deploy transformer models by optimally allocating Field Programmable Gate Array resources to each fundamental block of transformer-based models for maximum performance in terms of low latency and high throughput. This disclosure relates to a system and method which constructs a plurality of parameterized transformer model templates from input parameters comprising templates, one or more transformer-based models, one or more data types corresponding to one or more transformer-based models, table comprising one or more latency values and one or more resource utilization values and feedback mode. One or more values are assigned to each plurality of parameters comprised in a plurality of parameterized transformer model templates to obtain plurality of optimal parameters. An optimal template is obtained from the final template selector for each of the plurality of parameterized transformer model templates, having maximum performance in terms of low latency and maximum throughput.

Abstract: High-performance deployment of DNN recommendation models heavily rely on embedding tables, and their performance bottleneck lies in the latency of embedding access. To optimize the deployment of RMs, the method and system is disclosed, which leverages heterogeneous memory types on FPGAs to improve the overall performance by maximizing the availability of frequently accessed data in faster memory. The system, using a optimizer dynamically allocates table partitions of the embedding tables based on history of input access history. A pre-optimizer block disclosed determines whether smaller tables should be partitioned or placed entirely in smaller memories, improving overall efficiency. The performance of RM is improved with improvement in average embedding fetch latency and effectively inference latency via modified Round Trip computation.

Abstract: Works in the literature fail to leverage embedding access patterns and memory units' access/storage capabilities, which when combined can yield high-speed heterogeneous systems by dynamically re-organizing embedding tables partitions across hardware during inference. A method and system for optimal deployment of embeddings tables across heterogeneous memory architecture for high-speed recommendations inference is disclosed, which dynamically partitions and organizes embedding tables across fast memory architectures to reduce access time. Partitions are chosen to take advantage of the past access patterns of those tables to ensure that frequently accessed data is available in the fast memory most of the time. Partition and replication is used to co-optimize memory access time and resources.

Abstract: Works in the literature fail to leverage embedding access patterns and memory units' access/storage capabilities, which when combined can yield high-speed heterogeneous systems by dynamically re-organizing embedding tables partitions across hardware during inference. A method and system for optimal deployment of embeddings tables across heterogeneous memory architecture for high-speed recommendations inference is disclosed, which dynamically partitions and organizes embedding tables across fast memory architectures to reduce access time. Partitions are chosen to take advantage of the past access patterns of those tables to ensure that frequently accessed data is available in the fast memory most of the time. Partition and replication is used to co-optimize memory access time and resources.

Abstract: The disclosure generally relates to an FPGA-based online 3D bin packing. Online 3D bin packing is the process of packing boxes into larger bins-Long Distance Containers (LDCs) such that the space inside each LDC is used to the maximum extent. The use of deep reinforcement learning (Deep RL) for this process is effective and popular. However, since the existing processor-based implementations are limited by Von-Neumann architecture and take a long time to evaluate each alignment for a box, only a few potential alignments are considered, resulting in sub-optimal packing efficiency. This disclosure describes an architecture for bin packing which leverages pipelining and parallel processing on FPGA for faster and exhaustive evaluation of all alignments for each box resulting in increased efficiency. In addition, a suitable generic purpose processor is employed to train the neural network within the algorithm to make the disclosed techniques computationally light, faster and efficient.

Abstract: This disclosure relates generally to method and system to estimate performance of session based recommendation model layers on FPGA. Profiling is easy to perform on software based platforms such as a CPU and a GPU which have development frameworks and tool sets but on systems such as a FPGA, implementation risks are higher and important to model the performance prior to implementation. The disclosed method analyses a session based recommendation (SBR) model layers for performance estimation. Further, a network bandwidth is determined to process each layer of the SBR model based on dimensions. Performance of each layer of the SBR model is estimated at a predefined frequency by creating a layer profile comprising a throughput and a latency in one or more batches. Further, the method deploys an optimal layer on at least one of a heterogeneous hardware based on the estimated performance of each layer profile on the FPGA.

Abstract: The present invention relates to processes for preparing phenyl-pyrazoles of Formula (I) and salts and pharmaceutical compositions thereof, useful as modulators of 5-HT2A serotonin receptor activity. The present invention also relates to intermediates used in the processes, and their preparation. The present invention also relates to crystalline forms of 5-HT2A serotonin receptor modulators, compositions thereof and methods of using the same.

Abstract: The present invention relates to processes for preparing phenyl-pyrazoles of Formula (I) and salts and pharmaceutical compositions thereof, useful as modulators of 5-HT2A serotonin receptor activity. The present invention also relates to intermediates used in the processes, and their preparation. The present invention also relates to crystalline forms of 5-HT2A serotonin receptor modulators, compositions thereof and methods of using the same.

Abstract: The present invention relates to processes for preparing aromatic ether compounds that are modulators of glucose metabolism and therefore useful in the treatment of metabolic disorders such as diabetes and obesity.

Abstract: The present invention relates to improved imaging agents useful for .sup.19 F magnetic resonance imaging (MRI). More particularly, the present invention relates to fluorinated fatty acid sulfonate derivatives, such as those derived by reaction of fluorinated fatty acids with sulfonated compounds such as taurine analogs or isethionic acid analogs, which offer improved water solubility and biocompatibility.

Abstract: Organic compounds for diagnostic imaging which contain at least one aryl group which has been derivatized to contain at least one perfluoro-1H,1H-neopentyl moiety are provided. The perfluoro-1H,1H-neopentyl groups produce a single magnetic resonance to insure a maximum signal to noise ratio. One compound disclosed is 2-O-oleoylglycerol 1,3-bis(7'-{3",5"-di[2"',2"'-di(trifluoromethyl)3"', 3"',3"'-trifluoropropyl]phenyl}heptanoate). In the preferred embodiment, a lipid emulsion is provided as a carrier vehicle to deliver the derivitized analog to a mammalian recipient. Methods to use these compounds in MRI and computerized tomography are provided.