One variation of a method for deploying sensors within an agricultural facility includes: accessing scan data of a set of modules deployed within the agricultural facility; extracting characteristics of plants occupying the set of modules from the scan data; selecting a first subset of target modules from the set of modules, each target module in the set of target modules containing a group of plants exhibiting characteristics representative of plants occupying modules neighboring the target module; for each target module, scheduling a robotic manipulator within the agricultural facility to remove a particular plant from a particular plant slot in the target module and load the particular plant slot with a sensor pod from a population of sensor pods deployed in the agricultural facility; and monitoring environmental conditions at target modules in the first subset of target modules based on sensor data recorded by the first population of sensor pods.

Apparatus and method for leveraging simultaneous multithreading for bulk compute operations. For example, one embodiment of a processor comprises: a plurality of cores including a first core to simultaneously process instructions of a plurality of threads; a cache hierarchy coupled to the first core and the memory, the cache hierarchy comprising a Level 1 (L1) cache, a Level 2 (L2) cache, and a Level 3 (L3) cache; and a plurality of compute units coupled to the first core including a first compute unit associated with the L1 cache, a second compute unit associated with the L2 cache, and a third compute unit associated with the L3 cache, wherein the first core is to offload instructions for execution by the compute units, the first core to offload instructions from a first thread to the first compute unit, instructions from a second thread to the second compute unit, and instructions from a third thread to the third compute unit.

A technique for operating a processor includes receiving, at an issue queue, a store instruction that has an associated address generation (AGN) operation and an associated data operation. The AGN operation is issued to AGN logic associated with a pipeline slice in response to all source operands for the AGN operation being ready. The AGN logic is configured to generate an address for the store instruction. Confirmation, for the AGN operation is received. The confirmation includes an indication of the pipeline slice that performed the AGN operation. In response to receiving the confirmation and a source operand for the data operation being ready, the issue queue issues the data operation to data logic associated with the pipeline slice indicated by the confirmation. The data logic is configured to format data for the store instruction.

A method, executed by a computer, includes pairing a first core with a second core to form a first core group, wherein each core of the group has a plurality of functional units, transferring instructions received by the first core to the second core for execution via a first inter-core communication bus, and executing the instructions on the second core. A computer system and computer program product corresponding to the above method are also disclosed herein.

Operation of a multi-slice processor that includes a plurality of execution slices. Operation of such a multi-slice processor includes: receiving a first instruction indicating a first target register; receiving a second instruction indicating the first target register as a source operand; responsive to the second instruction indicating the first target register as a source operand, updating a dependent count corresponding to the first instruction; and issuing, in dependence upon the dependent count for the first instruction being greater than a dependent count for another instruction, the first instruction to an execution slice of the plurality of execution slices.

One embodiment provides for a graphics processing unit (GPU) to accelerate machine learning operations, the GPU comprising an instruction cache to store a first instruction and a second instruction, the first instruction to cause the GPU to perform a floating-point operation, including a multi-dimensional floating-point operation, and the second instruction to cause the GPU to perform an integer operation; and a general-purpose graphics compute unit having a single instruction, multiple thread architecture, the general-purpose graphics compute unit to concurrently execute the first instruction and the second instruction.

Operation of a multi-slice processor that includes a plurality of execution slices and a load/store superslice, where the load/store superslice includes a set predict array, a first load/store slice, and a second load/store slice. Operation of such a multi-slice processor includes: receiving a two-target load instruction directed to the first load/store slice and a store instruction directed to the second load/store slice; determining a first subset of ports of the set predict array as inputs for an effective address for the two-target load instruction; determining a second subset of ports of the set predict array as inputs for an effective address for the store instruction; and generating, in dependence upon logic corresponding to the set predict array that is less than logic implementing an entire load/store slice, output for performing the two-target load instruction in parallel with generating output for performing the store instruction.

Methods and apparatus for transmitting data between execution slices of a multi-slice processor including receiving, by an execution slice, a broadcast message comprising an instruction tag (ITAG) for a producer instruction, a latency, and a source identifier; determining that an issue queue in the execution slice comprises an ITAG for a consumer instruction, wherein the consumer instruction depends on result data from the producer instruction; calculating a cycle countdown using the latency and the source identifier; determining that the cycle countdown has expired; and in response to determining that the cycle countdown has expired, reading the result data from the producer instruction.

Apparatus and methods are disclosed for controlling execution of memory access instructions in a block-based processor architecture using a hardware structure that indicates a relative ordering of memory access instruction in an instruction block. In one example of the disclosed technology, a method of executing an instruction block having a plurality of memory load and/or memory store instructions includes selecting a next memory load or memory store instruction to execute based on dependencies encoded within the block, and on a store vector that stores data indicating which memory load and memory store instructions in the instruction block have executed. The store vector can be masked using a store mask. The store mask can be generated when decoding the instruction block, or copied from an instruction block header. Based on the encoded dependencies and the masked store vector, the next instruction can issue when its dependencies are available.

Systems and methods for predicting forward market pricing for renewable energy credit based on human behavioral data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market where the forecast is based at least in part on a human behavior information collected from at least one human behavioral data source. The example system may further include wherein the energy credit includes a renewable energy credit associated with a renewable energy system, and a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price of the energy credit.