Digital approximate squarer for machine learning
OwnerAli Tasdighi Far, Jerry M. Collingsinventors
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OwnerAli Tasdighi Far, Jerry M. Collingsinventors
Estimating the term…
Digital approximate squarer (aSQR)s utilizing apparatuses, circuits, and methods are described in this disclosure. The disclosed aSQR methods can operate asynchronously and or synchronously. For applications where low precisions is acceptable, fewer interpolations can yield less precise square approximation, which can be computed faster and with lower power consumption. Conversely, for applications where higher precision are required, more interpolations steps can generate more precise square approximation. By utilizing the disclosed aSQR method, precision objectives of a squarer approximation function can be programmed real-time and on the fly, which enables optimizing for power consumption and speed of squaring, in addition to optimize for the approximate squarer's die size and cost.
An approximate digital squaring (aSQR) method in a digital state machine in an integrated circuit (IC), the digital state machine in the integrated circuit (IC) comprising at least one of an input port, an output port, a digital memory apparatus, a digital adder apparatus, a digital subtractor apparatus, a digital divider apparatus, a digital accumulator apparatus, and a digital maximum selector apparatus, the method comprising:
A method of generating an approximate square (aSQR) of input signals in an approximate squarer integrated circuit (IC), the approximate squarer integrated circuit (IC) comprising at least one of a subtracting apparatus, a summation apparatus, a maximum-selecting apparatus, and a scaling apparatus, the method comprising:
Dependencies are inferred from the claim text.