14,358 research outputs found
A Search for Good Pseudo-random Number Generators : Survey and Empirical Studies
In today's world, several applications demand numbers which appear random but
are generated by a background algorithm; that is, pseudo-random numbers. Since
late century, researchers have been working on pseudo-random number
generators (PRNGs). Several PRNGs continue to develop, each one demanding to be
better than the previous ones. In this scenario, this paper targets to verify
the claim of so-called good generators and rank the existing generators based
on strong empirical tests in same platforms. To do this, the genre of PRNGs
developed so far has been explored and classified into three groups -- linear
congruential generator based, linear feedback shift register based and cellular
automata based. From each group, well-known generators have been chosen for
empirical testing. Two types of empirical testing has been done on each PRNG --
blind statistical tests with Diehard battery of tests, TestU01 library and NIST
statistical test-suite and graphical tests (lattice test and space-time diagram
test). Finally, the selected PRNGs are divided into groups and are
ranked according to their overall performance in all empirical tests
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning.
Driven by ML applications, the number of different ML inference systems has
exploded. Over 100 organizations are building ML inference chips, and the
systems that incorporate existing models span at least three orders of
magnitude in power consumption and five orders of magnitude in performance;
they range from embedded devices to data-center solutions. Fueling the hardware
are a dozen or more software frameworks and libraries. The myriad combinations
of ML hardware and ML software make assessing ML-system performance in an
architecture-neutral, representative, and reproducible manner challenging.
There is a clear need for industry-wide standard ML benchmarking and evaluation
criteria. MLPerf Inference answers that call. In this paper, we present our
benchmarking method for evaluating ML inference systems. Driven by more than 30
organizations as well as more than 200 ML engineers and practitioners, MLPerf
prescribes a set of rules and best practices to ensure comparability across
systems with wildly differing architectures. The first call for submissions
garnered more than 600 reproducible inference-performance measurements from 14
organizations, representing over 30 systems that showcase a wide range of
capabilities. The submissions attest to the benchmark's flexibility and
adaptability.Comment: ISCA 202
On the Duality of Probing and Fault Attacks
In this work we investigate the problem of simultaneous privacy and integrity
protection in cryptographic circuits. We consider a white-box scenario with a
powerful, yet limited attacker. A concise metric for the level of probing and
fault security is introduced, which is directly related to the capabilities of
a realistic attacker. In order to investigate the interrelation of probing and
fault security we introduce a common mathematical framework based on the
formalism of information and coding theory. The framework unifies the known
linear masking schemes. We proof a central theorem about the properties of
linear codes which leads to optimal secret sharing schemes. These schemes
provide the lower bound for the number of masks needed to counteract an
attacker with a given strength. The new formalism reveals an intriguing duality
principle between the problems of probing and fault security, and provides a
unified view on privacy and integrity protection using error detecting codes.
Finally, we introduce a new class of linear tamper-resistant codes. These are
eligible to preserve security against an attacker mounting simultaneous probing
and fault attacks
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