Article Accepted in IEEE Transactions on Computers

Manil Dev Gomony just had a journal article entitled “A Globally Arbitrated Memory Tree for Mixed-Time-Criticality Systems” accepted in the high-impact journal IEEE Transactions on Computers. This article extends a conference paper published at DATE in 2015 that was called “A Generic, Scalable and Globally Arbitrated Memory Tree for Shared DRAM Access in Real-Time Systems” that was published in collaboration with Jamie Garside and Neil Audsley from University of York. The original paper explained the design and efficient hardware implementation of a transaction arbiter for real-time systems that could be configured to behave like any of five well-known arbiters, i.e. TDM, Round Robin, Credit-Controlled Static Priority, Priority-Based Scheduler, and Frame-Based Static Priority. The key feature of the arbiter is that it is distributed, which means that accounting and enforcement is not done in a single centralized location, allowing it to scale to systems with many resource clients without negatively impacting the maximum frequency at which it operates.

The journal article extends the original conference paper by adding more detail and examples on the design of the memory tree, as well as improving positioning. However, it also extends the scope of the work to consider more complex Mixed-Time-Criticality systems where some clients are more concerned about average-case than worst-case performance. It also considers that the requirements of the clients may be diverse, i.e. that some may have high bandwidth requirements and are latency-tolerant, while others have low bandwidth requirements, but are latency-critical. This is diversity of requirements is addressed by showing how the memory tree supports the transaction arbiter to be chosen individually per client rather than once for the entire system. For example, some real-time clients may be configured by non-work-conserving TDM arbitration to get predictable bandwidth and latency while enjoying complete temporal isolation from other clients, which simplifies integration and certification. Other clients sharing the same resource, may be scheduled using e.g. using a work-conserving Frame-Based Static Priority scheduler to reflect an interest in low average latency while still distinguishing their relative latency-sensitivity. The memory tree supports any combination of the mechanisms discussed above, but we provide a formal analysis of the mixed arbitration algorithm explained above. The article demonstrates the benefits of this approach on a VHDL hardware implementation, as well as its cost in terms of area and power compared to centralized non-mixed arbitration policies by means of ASIC synthesis.

Journal Article Accepted in ACM TODAES

We just received the good news that Hazem’s article “ Reducing the Complexity of Dataflow Graphs using Slack-based Merging” has been accepted for publication in ACM Transactions on Design Automation of Electronic Systems (TODAES). The article addresses an important problem when working with synchronous data-flow (SDF) graphs, namely that the size of the graph explodes when transforming it to its equivalent homogeneous (HSDF) representation, which prevents any design or analysis algorithms requiring this transformation as a first step from scaling to larger graphs. In the scope of Hazem’s work, this has caused problems when converting an SDF graph into a set of independent periodic real-time tasks.

This article proposes a heuristic algorithm to reduce the size of the resulting HSDF graph prior to analysis by merging actors in the graph, thereby speeding up analysis algorithms using the resulting graph. Three key properties of the algorithm are: 1) it cannot violate the latency or throughput requirements of the original graph, 2) it cannot cause deadlock in the resulting merged graph, and 3) only HSDF actors corresponding to firings of the same SDF actor can be merged to enable the resulting merged graph to be efficiently used by mapping algorithms. The behavior of the algorithm is evaluated with applications from the SDF3 benchmark suite and it is compared to results of an optimal exhaustive merging algorithm for smaller graphs.