4,658 research outputs found
New formats for computing with real-numbers under round-to-nearest
An edited version of this work was accepted in IEEE Transactions on computers, DOI 10.1109/TC.2015.2479623In this paper, a new family of formats to deal with real number for applications requiring round to nearest is proposed.
They are based on shifting the set of exactly represented numbers which are used in conventional radix-R number systems.
This technique allows performing radix complement and round to nearest without carry propagation with negligible time and
hardware cost. Furthermore, the proposed formats have the same storage cost and precision as standard ones. Since conversion
to conventional formats simply require appending one extra-digit to the operands, standard circuits may be used to perform
arithmetic operations with operands under the new format. We also extend the features of the RN-representation system and
carry out a thorough comparison between both representation systems. We conclude that the proposed representation system
is generally more adequate to implement systems for computation with real number under round-to-nearest.Ministry of Education and Science of Spain under contracts TIN2013-42253-P
Measuring Improvement when Using HUB Formats to Implement Floating-Point Systems under Round-to-Nearest
MEC bajo TIN2013-42253-PThis paper analyzes the benefits of using HUB
formats to implement floating-point arithmetic under round-tonearest
mode from a quantitative point of view. Using HUB
formats to represent numbers allows the removal of the rounding
logic of arithmetic units, including sticky-bit computation. This
is shown for floating-point adders, multipliers, and converters.
Experimental analysis demonstrates that HUB formats and the
corresponding arithmetic units maintain the same accuracy as
conventional ones. On the other hand, the implementation of
these units, based on basic architectures, shows that HUB formats
simultaneously improve area, speed, and power consumption.
Specifically, based on data obtained from the synthesis, a HUB
single-precision adder is about 14% faster but consumes 38% less
area and 26% less power than the conventional adder. Similarly, a
HUB single-precision multiplier is 17% faster, uses 22% less area,
and consumes slightly less power than conventional multiplier. At
the same speed, the adder and multiplier achieve area and power
reductions of up to 50% and 40%, respectively
Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations
Although double-precision floating-point arithmetic currently dominates
high-performance computing, there is increasing interest in smaller and simpler
arithmetic types. The main reasons are potential improvements in energy
efficiency and memory footprint and bandwidth. However, simply switching to
lower-precision types typically results in increased numerical errors. We
investigate approaches to improving the accuracy of reduced-precision
fixed-point arithmetic types, using examples in an important domain for
numerical computation in neuroscience: the solution of Ordinary Differential
Equations (ODEs). The Izhikevich neuron model is used to demonstrate that
rounding has an important role in producing accurate spike timings from
explicit ODE solution algorithms. In particular, fixed-point arithmetic with
stochastic rounding consistently results in smaller errors compared to single
precision floating-point and fixed-point arithmetic with round-to-nearest
across a range of neuron behaviours and ODE solvers. A computationally much
cheaper alternative is also investigated, inspired by the concept of dither
that is a widely understood mechanism for providing resolution below the least
significant bit (LSB) in digital signal processing. These results will have
implications for the solution of ODEs in other subject areas, and should also
be directly relevant to the huge range of practical problems that are
represented by Partial Differential Equations (PDEs).Comment: Submitted to Philosophical Transactions of the Royal Society
Floating Point Square Root under HUB Format
Unit-Biased (HUB) is an emerging format based on
shifting the representation line of the binary numbers by half
unit in the last place. The HUB format is specially relevant
for computers where rounding to nearest is required because
it is performed simply by truncation. From a hardware point
of view, the circuits implementing this representation save both
area and time since rounding does not involve any carry propagation.
Designs to perform the four basic operations have been
proposed under HUB format recently. Nevertheless, the square
root operation has not been confronted yet. In this paper we
present an architecture to carry out the square root operation
under HUB format for floating point numbers. The results of
this work keep supporting the fact that the HUB representation
involves simpler hardware than its conventional counterpart for
computers requiring round-to-nearest mode.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tec
- …