Probabilistic Value-Deviation-Bounded Source-Dependent Bit-Level Channel Adaptation for Approximate Communication

Computing systems that can tolerate effects of errors in their communicated data values can trade this tolerance for improved resource efficiency. Many important applications of computing, such as embedded sensor systems, can tolerate errors that are bounded in their distribution of deviation from correctness (distortion). We present a channel adaptation technique which modulates properties of I/O channels typical in embedded sensor systems, to provide a tradeoff between I/O power dissipation and distortion of communicated data. We provide an efficient-to-compute formulation for the distribution of integer distortion accounting for the distribution of transmitted values. Using this formulation we implement our value-deviation-bounded (VDB) channel adaptation. We experimentally quantify the achieved reduction in power dissipation on a hardware prototype integrated with the required programmable channel modulation circuitry. We augment these experimental measurements with an analysis of the distributions of distortions. We show that our probabilistic VDB channel adaptation can provide up to a 2$\times$ reduction in I/O power dissipation. When synthesized for a miniature low-power FPGA intended for use in sensor interfaces, a register transfer level implementation of the channel adaptation control logic requires only 106 flip-flops and 224 4-input LUTs for implementing per-bit channel adaptation on serialized streams of 8-bit sensor data

Efficiency Limits for Value-Deviation-Bounded Approximate Communication

Transferring data between integrated circuits accounts for a growing proportion of system power in wearable and mobile systems. The dynamic component of power dissipated in this data transfer can be reduced by reducing signal transitions. Techniques for reducing signal transitions on communication links have traditionally been targeted at parallel buses and can therefore not be applied when the transfer interfaces are serial buses. In this article, we address the issue of the best-case effectiveness of techniques to reduce signal transitions on serial buses, if these techniques also allow some error in the numeric interpretation of transmitted data. For many embedded applications, exchanging numeric accuracy for power reduction is a worthwhile tradeoff. We present a study of the efficiency of these value-deviation-bounded approximate serial data encoders (VDBS data encoders) and proofs of their properties. The bounds and proofs we present yield new insights into the best possible tradeoffs between dynamic power reduction and approximation error that can be achieved in practice. The insights are important regardless of whether actual practical VDBS data encoders are implemented in software or in hardware

Perceived-Color Approximation Transforms for Programs that Draw

© 1981-2012 IEEE. Human color perception acuity is not uniform across colors. This makes it possible to transform drawing programs to generate outputs whose colors are perceptually equivalent but numerically distinct. One benet of such transformations is lower display power dissipation on organic light-emitting diode (OLED) displays. We introduce Ishihara, a language for 2D drawing that lets programs specify perceptual-color equivalence classes to use in drawing operations enabling compile-time and runtime transformations that trade perceived color accuracy for lower OLED display power dissipation

Error-efficient computing systems

This survey explores the theory and practice of techniques to make computing systems faster or more energy-efficient by allowing them to make controlled errors. In the same way that systems which only use as much energy as necessary are referred to as being energy-efficient, you can think of the class of systems addressed by this survey as being error-efficient: They only prevent as many errors as they need to. The definition of what constitutes an error varies across the parts of a system. And the errors which are acceptable depend on the application at hand. In computing systems, making errors, when behaving correctly would be too expensive, can conserve resources. The resources conserved may be time: By making some errors, systems may be faster. The resource may also be energy: A system may use less power from its batteries or from the electrical grid by only avoiding certain errors while tolerating benign errors that are associated with reduced power consumption. The resource in question may be an even more abstract quantity such as consistency of ordering of the outputs of a system. This survey is for anyone interested in an end-to-end view of one set of techniques that address the theory and practice of making computing systems more efficient by trading errors for improved efficiency

Efficient Programmable Random Variate Generation Accelerator from Sensor Noise

We introduce a method for non-uniform random number generation based on sampling a physical process in a controlled environment. We demonstrate one proof-of-concept implementation of the method that reduces the error of Monte Carlo integration of a univariate Gaussian by 1068 times while doubling the speed of the Monte Carlo simulation. We show that the supply voltage and temperature of the physical process must be controlled to prevent the mean and standard deviation of the random number generator from drifting.Alan Turing Institute award: TU/B/000096 EPSRC grants: EP/N510129/1, EP/R022534/1, EP/V004654/1 and EP/L015889/