Floating point multiplications
AssigneeOpenAI OpCo, LLC
InventorsCagla Cakir, Burak Erbagci, Alexander Almela Conklin, Tracey DellaRova, Mohammed Elneanaei Abdelmoneem Fouda, Jean-Didier Allegrucci
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AssigneeOpenAI OpCo, LLC
InventorsCagla Cakir, Burak Erbagci, Alexander Almela Conklin, Tracey DellaRova, Mohammed Elneanaei Abdelmoneem Fouda, Jean-Didier Allegrucci
Estimating the term…
Systems, apparatuses, and methods are disclosed for improved matrix-vector operations in accelerators that may be useful or heavy AI training and inference workloads. The disclosed technology provides arrangements that permit more efficient computation by, for example, performing calculations without repeated conversion between numeric domains. In some implementations, a compute-in-memory (CIM) macro is configured to perform a vector matrix multiplication (VMM) operation between a vector of activation values and a matrix of weight values, and a mode decoding unit is configured to provide a mode of the VMM operation according to a first floating point format of the activation values and a second floating point format of the weight values. A column cell of the CIM macro can be configured to output a product between an activation value and a weight value using a half adder and multiple multiplexers that provide selections to a full adder based on control signals.
A hardware accelerator comprising:
A method for performing a vector matrix multiplication (VMM) operation in a compute-in-memory (CIM) macro between a vector of activation values and a matrix of weight values, the method comprising:
Dependencies are inferred from the claim text.