Technologies for processing network packets in an agent-mesh architecture include a network interface controller (NIC) of a computing device configured to write, by a network fabric interface of a memory fabric of the NIC, a received network packet to the memory fabric in a distributed fashion. The network fabric interface is configured to send an event message indicating the received network packet to a packet processor communicatively coupled to the memory fabric. The packet processor is configured to read, in response to having received the generated event message, at least a portion of the received network packet from the memory fabric, identify an agent of the NIC for additional processing of the received network packet, generate a network packet received event message indicating the received network packet is available for processing, and transmit the network packet received event message to the identified agent. Other embodiments are described herein.

Compressed domain processors configured to perform operations on data compressed in a format that preserves order. The Compressed domain processors may include operations such as addition, subtraction, multiplication, division, sorting, and searching. In some cases, compression engines for compressing the data into the desired formats are provided.

Technologies for processing network packets by a network interface controller (NIC) of a computing device include a network interface, a packet processor, and a controller device of the NIC, each communicatively coupled to a memory fabric of the NIC. The packet processor is configured to receive an event message from the memory fabric and transmit a message to the controller device, wherein the message indicates the network packet has been received and includes the memory fabric location pointer. The controller device is configured to fetch at least a portion of the received network packet from the memory fabric, write an inbound descriptor usable by one or more on-die cores of the NIC to perform an operation on the fetched portion, and restructure the network packet as a function of an outbound descriptor written by the on-die cores subsequent to performing the operation. Other embodiments are described herein.

Technologies for encrypted data access by field-programmable gate array (FPGA) user kernels include a computing device having an FPGA and an external memory device accessible by the FPGA. The FPGA includes a secure key store, a micro-encryption engine, and multiple slots for user kernels that are each identifiable with an index. A user kernel is programmed at an index and a symmetric encryption key is provisioned to the secure key store at the index. The micro encryption engine may read encrypted data from the external memory device, decrypt the encrypted data with the key associated with the index of the user kernel, and forward plain text data to the user kernel. The micro encryption engine may also receive plain text data from the user kernel, encrypt the plain text data with the key, and write the encrypted data to the external memory device. Other embodiments are described and claimed.

A system for encoding and decoding data-tokens. In some examples, the system may be configured to encode and decode integers. In other cases, the system may be configured to encode and decode symbols or bytes of data.

Detailed herein are embodiments of systems, methods, and apparatuses for decompression using hardware and software. In hardware, an input buffer stores incoming input records from a compressed stream. A plurality of decoders decode at least one input record from the input buffer out output an intermediate record from the decoded data and a subset of the plurality of decoders to output a stream of literals. Finally, a reformat circuit formats an intermediate record into one of two types of tokens.

Technologies for high-ratio compression with heterogeneous history buffers include a computing device having an accelerator complex with a large history buffer and a small history buffer. The large history buffer has a larger size than the small history buffer. For example, the small history buffer may be 32 kilobytes and the large history buffer may be 64 kilobytes, 1 megabyte, or larger. The large history buffer is coupled to a large-buffer compare core that searches for matches in the large history buffer, finds a best match, and forwards the best match to a small-buffer compare core. The small-buffer compare core searches the small history buffer for matches, receives the match forwarded from the large-buffer compare core, and determines a best match from the matches in the small history buffer and the forwarded match. Other embodiments are described and claimed.

Methods and systems for processing telemetry data that contains multiple data types is disclosed. Optimum multimodel encoding approaches can be used which can achieve data-specific compression performance for heterogeneous datasets by distinguishing data types and their characteristics at real-time and applying most effective compression method to a given data type. Using an optimum encoding diagram for heterogeneous data, a data classification algorithm classifies input data blocks into predefined categories, such as Unicode, telemetry, RCS and IR for telemetry datasets, and a class of unknown which includes non-studied data types, and then assigns them into corresponding compression models.

Technologies for database acceleration include a computing device having a database accelerator. The database accelerator performs a decompress operation on one or more compressed elements of a compressed database to generate one or more decompressed elements. After decompression of the compressed elements, the database accelerator prepares the one or more decompressed elements to generate one or more prepared elements to be processed by an accelerated filter. The database accelerator then performs the accelerated filter on the one or more prepared elements to generate one or more output elements. Other embodiments are described and claimed.

Methods, apparatus, systems and articles of manufacture are disclosed for sparse tensor storage for neural network accelerators. An example apparatus includes sparsity map generating circuitry to generate a sparsity map corresponding to a tensor, the sparsity map to indicate whether a data point of the tensor is zero, static storage controlling circuitry to divide the tensor into one or more storage elements, and a compressor to perform a first compression of the one or more storage elements to generate one or more compressed storage elements, the first compression to remove zero points of the one or more storage elements based on the sparsity map and perform a second compression of the one or more compressed storage elements, the second compression to store the one or more compressed storage elements contiguously in memory.