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2 results about "Logarithmic growth" patented technology

In mathematics, logarithmic growth describes a phenomenon whose size or cost can be described as a logarithm function of some input. e.g. y = C log (x). Note that any logarithm base can be used, since one can be converted to another by multiplying by a fixed constant. Logarithmic growth is the inverse of exponential growth and is very slow. A familiar example of logarithmic growth is a number, N, in positional notation, which grows as logb (N), where b is the base of the number system used, e.g. 10 for decimal arithmetic.

Digital hardware circuit for efficient reduction operations using parallel matrix comparison

A digital hardware circuit and method for performing reduction operations that achieves constant timing depth regardless of input count. The invention replaces conventional sequential binary tree approaches with a parallel matrix comparison architecture where multiple comparator circuits simultaneously compare input signals against each other. Combinatorial logic circuits process comparison outputs to generate dominance signals indicating which input satisfies the reduction criteria, and selection logic outputs the final result. The parallel approach maintains exactly three logic levels regardless of number of processing inputs, enabling significantly higher clock frequencies than conventional methods whose timing depth increases logarithmically with input count. Applications include matrix multiplication engines, floating-point arithmetic units, and artificial intelligence accelerators where reduction operations for maximum / minimum finding, normalization, and other computations are performed millions of times per second. The constant timing depth enables operation at frequencies exceeding 1 GHz while providing scalable performance.
Owner:NEXTSILICON LTD

Systems and Methods for Incrementally Verifying Distributed Computation Across Multiple Untrusted Nodes

A method and system for verifying distributed computations across multiple untrusted computing nodes. A setup entity generates cryptographic reference strings including succinct non-interactive arguments (SNARGs) and batch arguments (seBARGs) for multiple proof hierarchy levels. Prover entities compute state transitions using nondeterministic inputs, generate proofs for individual computation steps, and merge proofs hierarchically to create compact proofs whose size grows logarithmically with computation length rather than linearly. A verifier entity validates the merged proofs at each level. In a zero-knowledge variant, the setup entity additionally generates encryption keys and zero-knowledge proof parameters. Prover entities encrypt all computation states and wrap proofs in zero-knowledge layers, hiding sensitive intermediate values and witnesses while maintaining verifiability.
Owner:NTT RESEARCH INC