Lev Denisov

Approximate computing techniques, such as precision tuning, are widely recognized as key enablers for the next generation of computing systems, where computation quality metrics play an important role. In precision tuning, a trade-off between the accuracy of computations and latency (and/or energy) is established, but identifying the opportunities for applying this approximate computing technique is often challenging. In this article, we compare two different approaches — worst-case static annotation and profile-guided annotation — and their implications when used in a precision tuning framework. To ensure a fair comparison, we implement the profile-guided approach in an existing tool, TAFFO, and experimentally compare it to the original static approach used by the tool. We validate our considerations using the well-known PolyBench/C benchmark suite, and two real-world application case studies. Our findings demonstrate that the profile-guided approach, fed with reasonable profiling data, in addition to needing less expertise to employ, delivers comparable speedup and better accuracy than the static approach.

Energy and performance optimization of embedded hardware and software is of critical importance to achieve the overall system goals. In this work, we study the optimization of memory access through a combination of hardware (Direct Memory Access, DMA) and software (Precision Tuning) techniques, and we propose a compiler toolchain for managing both in the context of heterogeneous RISC-Vbased platforms. Our proposed toolchain, SeTHet, enables 3 --- 48 × speedup over the baseline system when employing both DMA and precision tuning, regardless of the availability of floating point units in hardware. SeTHet also achieves up to 16× speedup compared to DMA alone, thus proving that the combination of the two techniques provides a major improvement over either technique employed in isolation.

In this study, we compare two different approaches to precision tuning --- worst-case static annotation, and profile-guided annotation. We propose and implement a profile-guided approach in an existing precision tuning tool TAFFO. In comparison to the static approach used by the tool before, the profile-guided approach reaches up to 4 orders of magnitude lower error while providing comparable speedup and requiring less expertise from the programmer, tested on benchmarks of the PolyBench/C suite.