In this paper, we describe TOGAPS, a Testability-Oriented Genetic Algorithm for Pipeline
Synthesis. The input to TOGAPS is an unscheduled data flow graph along with a specification
of the desired pipeline latency. TOGAPS generates a register-level description of a
datapath which is near-optimal in terms of area, meets the latency requirement, and is highly
testable. Genetic search is employed to explore a 3-D search space, the three dimensions
being the chip area, average latency, and the testability of the datapath. Testability of a
design is evaluated by counting the number of self-loops in the structure graph of the data
path. Each design is characterized by a four-tuple consisting of (i) the latency and schedule
information, (ii) the module allocation, (iii) operation-to-module binding, and (iv) value-to-register
binding. Accordingly, we maintain the population of designs in a hierarchical manner.
The topmost level of this hierarchy consists of the latency and schedule information,
which together characterize the timing performance of the design. The middle level of the
hierarchy consists of a number of allocations for a given latency/schedule duplet. The lowest
level of the hierarchy consists of a number of bindings for a specific latency/schedule/
allocation. An initial population of designs is constructed from the given data flow graph
using different latency cycles whose average latency is in the specified range. Multiple
scheduling heuristics are used to generate schedules for the DFG. For each of the resulting
scheduled data flow graphs, we decide on an allocation of modules and registers based on a
lower bound estimated using the schedule and latency information. The operation-to-module
binding and the value-to-register binding are then carried out. A fitness measure is evaluated
for each of the resulting data paths; this fitness measure includes one component for each of
the three search dimensions. Crossover and mutation operators are used to generate new
designs from the current set of parent designs. The crossover operator attempts to combine
the properties of two designs. The mutation operators include addition and deletion of pure
delays before scheduling, as well as changes in the register and module allocation prior to
binding. The genetic algorithm applies the rule of the survival of the fittest to obtain nearoptimal
solution to the otherwise intractable problem of data path synthesis. We have implemented
TOGAPS on a Sun/SPARC 10 and studied its performance on a number of
benchmark examples. Results indicate that TOGAPS finds area-optimal datapaths for the
specified latency cycle, while reducing the number of self-loops in the data path.
A Makespan Optimization Scheme for NP-Hard Gari Processing Job Scheduling Using Improved Genetic Algorithm