Soft Scheduling for Hardware

Soft Scheduling for Hardware
Richard Sharp1,2 and Alan Mycroft1
Computer Laboratory, Cambridge University
New Museums Site, Pembroke Street, Cambridge CB2 3QG, UK
AT&T Laboratories Cambridge
24a Trumpington Street, Cambridge CB2 1QA, UK
Abstract. Hardware designs typically combine parallelism and resourcesharing; a circuit’s correctness relies on shared resources being accessed
mutually exclusively. Conventional high-level synthesis systems guarantee mutual exclusion by statically serialising access to shared resources
during a compile-time process called scheduling. This approach suffers
from two problems: (i) there is a large class of practical designs which
cannot be scheduled statically; and (ii) a statically fixed schedule removes some opportunities for parallelism leading to less efficient circuits.
This paper surveys the expressivity of current scheduling methods and
presents a new approach which alleviates the above problems: first scheduling logic is automatically generated to resolve contention for shared resources dynamically; then static analysis techniques remove redundant
scheduling logic.
We call our method Soft Scheduling to highlight the analogy with Soft
Typing: the aim is to retain the flexibility of dynamic scheduling whilst
using static analysis to remove as many dynamic checks as possible.
At the structural level a hardware design can be seen as a set of interconnected
resources. These resources run concurrently and are often shared.
The interaction between parallelism and resource-sharing leads to an obvious problem: how does one ensure that shared resources are accessed mutually
exclusively? Existing silicon compilers solve the mutual exclusion problem by
statically serialising operations during a compile-time scheduling phase (see Section 1.1). This paper describes an alternative approach:
We automatically generate circuitry to perform scheduling dynamically
in a manner which avoids deadlock. Efficient circuits are obtained by
employing static analysis to remove redundant scheduling logic.
Our method is to scheduling as Soft Typing [4] is to type checking (see
Figure 1): the aim is to retain the flexibility of dynamic scheduling whilst using
No dynamic checks required in object code.
Not all valid programs pass type
Dynamic checking of argument
types required each time a function
is called.
All valid programs can be run.
No scheduling logic required in final circuit.
Not all valid programs can be
scheduled statically.
Scheduling logic required on each
shared resource in the final circuit.
All valid programs can be scheduled.
Less scheduling logic required
Fewer dynamic checks required
(some removed statically).
(some removed statically).
All valid programs can be schedAll valid programs can be run.
Fig. 1. An Informal Comparison Between Soft Scheduling and Soft Typing
static analysis to remove as many dynamic checks as possible. To highlight this
analogy we choose to call our method Soft Scheduling.
Although this paper considers the application of Soft Scheduling to hardware
synthesis, the technique is also applicable to software compilation. Aldrich et
al. [1] advocate a similar approach which uses static analysis to remove redundant
synchronization from Java programs.
Conventional High-Level Synthesis
The hardware community refer to high-level, block-structured languages as behavioural . At a lower level, structural languages describe a circuit as a set of
components, such as registers and multiplexers connected with wires and buses
(e.g. RTL Verilog [9]). High-level synthesis (sometimes referred to as behavioural
synthesis) is the process of compiling a behavioural specification into a structural
hardware description language.
A number of behavioural synthesis systems have been developed for popular
high-level languages (e.g. CtoV [20] and Handel [17]). Such systems typically
translate high-level specifications into an explicitly parallel flow-graph representation where allocation, binding and scheduling [6] are performed:
– Allocation is typically driven by user-supplied directives and involves choosing which resources will appear in the final circuit (e.g. 3 adders, 2 multipliers
and an ALU).
– Binding is the process of assigning operations in the high-level specification
to low-level resources—e.g. the + in line 4 of the source program will be
computed by adder 1 whereas the + in line 10 will be computed by the ALU.
– Scheduling involves assigning start times to operations in the flow-graph such
that no two operations will attempt to access a shared resource simultaneously. Mutually-exclusive access to shared resources is ensured by statically
serialising operations during scheduling.
The Contributions of This Paper
1. In contrast to conventional scheduling, we describe a method which generates logic to schedule shared resources dynamically. We show that (i ) our
approach is more expressive: all valid programs can be scheduled; and (ii ) in
some cases, our approach can generate more efficient designs by exploiting
parallelism possibilities removed by static scheduling.
2. We describe a high-level static analysis that enables us to remove redundant
scheduling logic and show that this can significantly improve the efficiency
of generated circuits.
We have implemented Soft Scheduling as part of the FLaSH Synthesis System
(see Section 1.2)—a novel hardware synthesis package being developed in conjunction with Cambridge University and AT&T Laboratories Cambridge. This
paper presents Soft Scheduling in the framework of the FLaSH silicon compiler.
The FLaSH Synthesis System
In previous work we introduced a hardware description language, SAFL [15]
(Statically Allocated Functional Language), and sketched its translation to hardware. An optimising silicon compiler (called FLaSH [18]—Functional Languages
for Synthesising Hardware) has been implemented to translate SAFL into hierarchical RTL Verilog. The system has been tested on a number of designs,
including a small commercial processor1 .
Although, for expository purposes, this paper describes our method in the
framework of SAFL, Soft Scheduling techniques are applicable to any high-level
Hardware Description Language which allows function definitions to be treated
as shared resources (e.g. HardwareC [11], Balsa [7], Tangram [2]). Indeed we
have extended SAFL with π-calculus [14] style channels and assignment without
modifying the Soft Scheduling phase [19].
Outline of Paper The remainder of this paper is structured as follows: Section 2 surveys existing scheduling techniques and explains the motivation for our
research; the SAFL language and its translation to hardware are briefly outlined
in Section 3; Section 4 presents the technical details of Soft Scheduling; some
practical examples are described in Section 5.
Comparison With Other Work
Traditional high-level synthesis packages perform scheduling using a data-structure
called a sequencing graph—a partial ordering which makes dependencies between
The instruction set of Cambridge Consultants XAP processor was implemented (see We did not include the SIF instruction (a form of debugging
breakpoint which transfers data to or from internal registers via a separately clocked
serial interface).
operations explicit. Recall that, in this context, scheduling is performed by assigning a start time to each operation in the graph such that operations which
invoke a shared resource do not occur in parallel [6]. There are a number of
problems with this approach:
1. The time taken to execute each operation in the sequencing graph must be
bounded statically (and in general padded to this length). This restriction
means that conventional scheduling techniques are not expressive enough to
handle a large class of practical designs. For example, it is impossible to
statically schedule an operation to perform a bus transaction of unknown
2. Since operations are scheduled statically one must be pessimistic about what
may be invoked in parallel in order to achieve safety. This can inhibit parallelism in the final design by unnecessarily serialising operations.
Ku and De Micheli have proposed Relative Scheduling [10] which extends the
method outlined above to handle operations with statically unbounded computation times. Their technique partitions a flow-graph into statically-schedulable
segments separated by anchor nodes—nodes which have unbounded execution
delays. Each segment is scheduled separately at compile-time. Finally, the compiler connects segments together by generating logic to signal the completion of
anchor nodes dynamically.
In [12] Ku and De Micheli show how Relative Scheduling of shared resources is
integrated into their Olympus Hardware Synthesis System [5]. Their method permits the scheduling of operations whose execution time is not statically bounded,
hence alleviating Problem 1 (above). However, potential contention for shared
resources is still resolved by serialising operations at compile time so Problem 2
remains. Furthermore, there is still a class of practical designs which cannot be
scheduled by Olympus. Consider the following example.
Using || as a parallel composition operator and assuming suitable definitions
of procedures Processor, DMA Controller and Memory we would essentially like
to describe the system of Figure 2 as:
Processor() || DMA_Controller() || Memory()
Since the operations corresponding to the invocation of the Processor and
DMA Controller both access a shared resource (Memory) the Olympus Synthesis
System requires that the calls must be serialised. However, if neither the call
to Processor() nor the call to DMA Controller terminate 2 , attempting to sequentialise the operations is futile; the correct operation of the system relies on
their parallel interleaving. Soft Scheduling is expressive enough to cope with nonterminating operations: the FLaSH compiler automatically generates an arbiter
to ensure mutually exclusive access to the Memory whilst allowing the Processor
and DMA Controller to operate in parallel (see Figure 2.ii). The following table
summarises the expressivity of various scheduling methods.
This is not merely a contrived example. In real designs both Processors and DMA
Controllers are typically non-terminating processes which constantly update the machine state.
(i) Data path
(ii) Control path
Fig. 2. A hardware design containing a memory device shared between a DMA controller and a processor
Bounded execution
Unbounded execution
Although the technique of using arbiters to protect shared resources is widely
employed, current hardware synthesis packages require arbitration to be coded
at the structural level on an ad hoc basis. Since arbitration can impose an overhead both in terms of chip area and time, programmers often try to eliminate
unnecessary locking operations manually. For large designs this is a tedious and
error-prone task which often results in badly structured and less reusable code.
In contrast the Soft Scheduling approach analyses a behavioural specification, automatically inserting arbiters on a where-needed basis. This facilitates readable
and maintainable source code without sacrificing efficiency.
This paper does not discuss the SAFL language in depth. A detailed comparison of SAFL with other hardware description languages including Verilog,
VHDL, MuFP, Lava, ELLA and Lustre can be found in [18].
An Overview of The SAFL Language
SAFL is a language of first order recurrence equations with an ML [13] style
syntax. A user program consists of a sequence of function definitions:
fun f1 (~x) = e1 ; . . . ; fun fn (~x) = en
Programs have a distinguished function, main, (usually fn ) which represents an
external world interface—at the hardware level it accepts values on an input
port and may later produce a value on an output port. The abstract syntax of
SAFL expressions, e, is as follows (we abbreviate tuples (e1 , . . . , ek ) as ~e and
similarly (x1 , . . . , xk ) as ~x):
– variables: x; constants: c;
– user function calls: f (~e);
– primitive function calls: a(~e)—where a ranges over primitive operators (e.g.
+, -, <=, && etc.);
– conditionals: e1 ? e2 : e3 ; and
– let bindings: let ~x = ~e in e0 end
In order to distinguish distinct call sites we assume that each abstract-syntax
node is labelled with a unique identifier, α, writing f α (e1 , . . . , ek ) to indicate a
call to function f at abstract-syntax node α.
Although functions can call other previously defined functions arbitrarily, the
only form of recursion allowed is tail-recursion. This allows us to statically allocate the storage (e.g. registers and memories) required by a SAFL program [15].
Tail recursive calls are compiled into feedback loops at the circuit level.
SAFL is a call-by-value language. All function-call arguments and let-definiens
are evaluated in parallel. Operations can be sequenced using the let construct
since the language semantics state that all let-declarations must terminate before the let-body is evaluated.
We compile SAFL to hardware in a resource aware manner. That is each
function definition is mapped into a single hardware-level resource; functions
which are called more than once become shared resources. For example, consider
the following SAFL code:
fun mult(x, y, acc) =
if (x=0 or y=0) then acc
else mult(x<<1, y>>1, if y.bit0 then acc+x else acc)
fun square(x) = mult(x, x, 0)
fun cube(x)
= mult(x, mult(x, x, 0), 0)
This SAFL specification describes a circuit containing a single shift-add multiplier shared between hardware-blocks to compute squares and cubes. Notice how
in contrast to traditional high-level synthesis (see Section 1.1) the resource aware
interpretation of SAFL specifications explicitly contains allocation and binding
information. (Although not of direct relevance to this paper, in [15] we show
how fold/unfold transformations [3] can be used to explore various allocation
and binding constraints.)
Translating SAFL to Hardware
As in Relative Scheduling [10] the FLaSH compiler generates logic to explicitly
signal the completion of operations. More precisely, each SAFL function definition, f , is compiled into a single resource, Hf , consisting of:
– logic to compute its body expression
– multiple control and data inputs: one control/data input-pair for each call
– multiple control outputs (one to return control to each caller)
– a single data output (which is shared between all callers)
An example of function connectivity is given in Figure 3. In this example
resource Hf is shared between Hg and Hh . Notice how Hf ’s data output is
shared, but the control structure is duplicated on a per call basis.
To perform a call to resource Hf the caller places the argument values on its
data input into Hf before triggering a call event on the corresponding control
input. Some point later, when Hf has finished computing, the result of the
call is placed on Hf ’s shared data-output and an event is generated on the
corresponding control output. Full details of the translation to hardware can be
found in [18].
Soft Scheduling: Technical Details
To protect shared resources the FLaSH compiler automatically generates scheduling logic to resolve conflicts dynamically (see Figure 3). The scheduling circuitry
consists of two parts: (i ) an arbiter to select which caller to service; and (ii ) a
locking mechanism to ensure the resource is accessed mutually exclusively. For
the sake of brevity, this paper uses the term arbiter to refer to both the arbiter
and locking structure.
Our approach is the hardware equivalent of using binary semaphores to protect critical regions in multi-threaded software. The analogy between arbiters
and semaphores is explored further in [15] where a compilation function from
SAFL to software is presented.
Removing Redundant Arbiters
Just because a resource is shared does not necessarily mean that arbitration is
required. For example consider the following SAFL program:
fun f(x) = ...
fun g(x) = f(f(x))
In this case, the two calls to f cannot occur in parallel: the innermost call must
complete before the outermost call can begin (recall that SAFL is a call-by-value
language). We do not need to generate an arbiter to serialise the calls to Hf :
from the structure of the program we can statically determine that the two calls
will not try to access f simultaneously.
We use Parallel Conflict Analysis (see Section 4.2) in order to detect redundant arbiters. Removing unnecessary arbitration is important for two reasons:
arbiter and
locking circuit
Function: f
Function: g
Function: h
Fig. 3. A structural diagram of the hardware circuit corresponding to a shared function,
f , called by functions g and h. Data buses are shown as thick lines, control wires as
thin lines.
1. Arbitration takes time: in the current version of the FLaSH compiler arbitration adds one cycle latency to a call even if the requested resource is
available at the time of call. Although we may accept this latency if it is
small in comparison to the callee’s average execution time, consider the case
where the callee is a frequently used resource with a small execution delay. In
this case an arbiter may significantly degrade the performance of the whole
system (see Example 5.1).
2. Arbitration uses chip area: although the gate-count of an arbiter is typically
small compared to the resource as a whole, the extra wiring complexity
required to represent request and grant signals adds to the area of the final
Arbiters are inserted at the granularity of calls. This offers increased performance over inserting arbiters on a per-resource basis. For example, in a design
containing a function, f , shared between five callers, we may infer that only two
calls to f require an arbiter—the other three calls need not suffer the overhead
of arbitration.
Parallel Conflict Analysis (PCA)
Parallel Conflict Analysis (PCA) is performed over the structure of a whole
SAFL program in order to determine which function calls may occur in parallel.
If a group of calls to the same function may occur in parallel then we say that
the group is conflicting. We only need to synthesise logic to arbitrate between
conflicting calls since, by definition, if a call f α is not in a conflicting group then
no other call to f can occur in parallel with f α .
The result of PCA is a conflict set: a set of calls which require arbiters.
For example, if the resulting conflict set is {f 1 , f 2 , f 5 , g 10 , g 14 } then we would
synthesise two arbiters: one for the conflicting group {f 1 , f 2 , f 5 }, the other for
conflicting group {g 10 , g 14 }.
We now proceed to define PCA. Let ef represent the body of function f .
Let the predicate RecursiveCall (f α ) hold iff f α is a recursive call (i.e. f α occurs
within the body of f ). C[[e]] returns the set of non-recursive calls which may
occur as a result of evaluating expression e:
C[[x]] = ∅
C[[c]] = ∅
C[[a(e1 , . . . , ek )]] =
C[[ei ]]
C[[f α (e1 , . . . , ek )]] = (
C[[ei ]] ) ∪
C[[if e1 then e2 else e3 ]] =
C[[ei ]]
C[[ei ]]
if RecursiveCall (f α )
{f } ∪ C[[ef ]] otherwise
C[[let ~x = ~e in e0 ]] =
PC (S1 , . . . , Sn ) takes sets of calls, (S1 , . . . , Sn ), and returns the conflict set resulting from the assumption that calls in each Si are evaluated in parallel with
calls in each Sj (j 6= i):
PC (S1 , . . . , Sn ) =
{f α ∈ Si | ∃β. f β ∈ Sj }
We are now able to define A[[e]] which returns the conflict set due to expression e:
A[[x]] = ∅
A[[c]] = ∅
A[[a(e1 , . . . , ek )]] = PC (C[[e1 ]], . . . , C[[ek ]]) ∪
A[[ei ]]
A[[ei ]]
A[[ei ]]
A[[f (e1 , . . . , ek )]] = PC (C[[e1 ]], . . . , C[[ek ]]) ∪
A[[if e1 then e2 else e3 ]] =
A[[ei ]]
A[[let ~x = ~e in e0 ]] = PC (C[[e1 ]], . . . , C[[ek ]]) ∪
Finally, for a program, p, consisting of a sequence of user-function definitions:
fun f1 (. . .) = e1 ; . . . ; fun fn (. . .) = en
A[[p]] returns the conflict set resulting from program, p. The letter A is used
since A[[p]] represents the calls which require arbiters:
A[[p]] =
A[[ek ]]
Notice that the equation for C[[f α (e1 , . . . , ek )]] is a little unusual in that it
is not defined compositionally. This reflects the fact that PCA depends on the
global structure of a whole SAFL program as opposed to just the local structure
of a function definition. C[[ · ]] is well-defined due to the predicate RecursiveCall
and the source restrictions on SAFL which ensure that the call-graph is acyclic.
Integrating PCA into the FLaSH Synthesis System
After computing A[[p]] at the abstract-syntax level the FLaSH Synthesis System
translates p into an intermediate flow-graph representation which makes both
control and data paths explicit [18]. At this level, the Call-nodes which require
arbitration are tagged (i.e. we tag node, n, iff n represents Call f α and f α ∈
When the circuit for Hf is generated only tagged calls to f are fed through
an arbiter, other calls are merely multiplexed. If none of the calls to f are in A[[p]]
then Hf ’s arbiter is eliminated completely. As Section 5.1 shows, using Parallel
Conflict Analysis to remove redundant arbitration can significantly improve the
performance of a large class of designs.
Avoiding Deadlock
Deadlock occurs when there is a cycle of blocked processes each waiting for a lock
held by the next process in the cycle. In the context of SAFL, where functions
represent hardware-level resources, a deadlocked cycle of resources can only occur
if we permit cycles in the call-graph (i.e. if we permit mutual recursion). Note
that we do not have to worry about self-tail-recursion since it is simply treated
as local loops and does not require locks.
Although the details are beyond the scope of this paper, in [15] we show
how to deal with mutual recursion whilst avoiding deadlock. For the purposes
of this paper it suffices to say that deadlock can be avoided simply by rejecting
mutually recursive SAFL programs.
Examples and Results
We provide three practical examples of applying Soft Scheduling to SAFL hardware designs. Each example illustrates a different point: Example 5.1 demonstrates that using static analysis to remove redundant arbiters is critical to
achieving efficient circuits; Example 5.2 highlights the extra expressivity of Soft
Scheduling over static scheduling techniques; Example 5.3 shows that dynamically controlling access to shared resources can lead to better performance than
generating a single schedule statically.
Parallel FIR Filter
Finite Impulse Response (FIR) filters are commonly used in Digital Signal
Processing where they are used to remove certain frequencies from a discretetime sampled signal. Assuming the existence of functions read next value and
write value, an integer arithmetic FIR filter can be described in SAFL as follows:
fun mult1(x,y) = x*y
fun mult2(x,y) = x*y
fun FIR(x,y,z,w) =
let val o1 = mult1(x,2)
val o2 = mult2(y,3)
val next = read_next_value()
let val o3 = mult1(x,7)
val o4 = mult2(y,9)
in write_value(o1 + o2 + o3 + o4);
Recall that the semantics of the let statement requires all val-declarations
to be computed fully before the body is executed (see Section 3). Although
this design contains two shared combinatorial multipliers, mult1 and mult2, the
outermost let statement ensures that the calls to the shared multipliers do not
occur in parallel. As a result Parallel Conflict Analysis infers that no arbitration
is required.
The shared combinatorial multipliers, mult1 and mult2 take a single cycle to
compute their result. Generating an arbiter for a shared resource adds an extra
cycle latency to each call (irrespective of whether the resource is busy at the
time of call). Thus, in this case, if we naively generated arbiters for all shared
resources, the performance of the multipliers would be degraded by a factor of
This example illustrates the importance of using static analysis to remove
redundant arbiters. For this design, using Parallel Conflict Analysis to remove
unnecessary arbiters leads to a 50% speed increase over a policy which simply
inserts arbiters on each shared resource.
Shared-Memory Multi-Processor Architecture
Figure 4 contains SAFL code fragments describing a simple shared-memory
multi-processor architecture. The system consists of two processors which have
type Instruction = {opcode:4,operand:12}
const WRITE=1, READ=0
extern Shared_memory(WriteSelect:1, Address:12, Data:16) : 16
extern instruction_mem1(Address:12) : 16
extern instruction_mem2(Address:12) : 16
(* Processor 1: Loads instructions from instruction_mem1 *)
fun proc1(PC:12, RX:16, RY:16, A:16) : unit =
let val instr:Instruction = instruction_mem1(PC)
val incremented_PC = PC + 2
case instr.opcode of
1 => (* Load Accumulator From Register *)
if instr.operand=1
then proc1(incremented_PC,RX,RY,RX)
else proc1(incremented_PC,RX,RY,RY)
| 2 => (* Load Accumulator From Memory *)
let val v = Shared_memory(READ, instr.operand, 0)
in proc1(incremented_PC,RX,RY,v)
| 3 => (* Store Accumulator To Memory *)
(Shared_memory(WRITE, instr.operand, A);
... etc
(* Processor 2: Loads instructions from instruction_mem2 *)
fun proc2(PC:12, RX:16, RY:16, A:16) : unit =
let val instr:Instruction = instruction_mem2(PC)
val incremented_PC = PC + 2
case instr.opcode of
| 2 => (* Load Accumulator From Memory *)
let val v = Shared_memory(READ, instr.operand, 0)
in proc2(incremented_PC,RX,RY,v)
| 3 => (* Store Accumulator To Memory *)
(Shared_memory(WRITE, instr.operand, A);
... etc
fun main() : unit = proc1(0,0,0,0) || proc2(0,0,0,0)
Fig. 4. Extracts from a SAFL program describing a shared-memory multi-processor
separate instruction memories but share a data memory. Such architectures are
common in control-dominated embedded systems where multiple heterogenous
processors perform separate tasks using a common memory to synchronise on
shared data structures.
The example starts by defining the type of instructions (records containing 4-bit opcodes and 12-bit operands), declaring 2 constants and specifying
the signatures of various (externally defined) memory functions. Bit-widths are
specified explicitly using notation X:n to indicate that variable X represents an
n-bit value. The bit-widths of function return values are also specified in this
way (unit indicates a width of 0).
The function Shared memory takes three arguments: WriteSelect indicates
whether a read or a write is to be performed; Address specifies the memory
location concerned; Data gives the value to be written (this argument is ignored
if a read operation is performed). It always returns the value of memory location
Functions proc1 and proc2 define two simple 16-bit processors. Argument
PC represents the program counter, RX and RY represent processor registers and
A is the accumulator. The processor state is updated on recursive calls—neither
processor terminates.
The main function initialises the system by calling proc1 and proc2 in parallel with PC, RX, RY and A initialised to 0.
Since the SAFL code contains parallel non-terminating calls to proc1 and
proc2 both of which share a single resource, neither static nor relative scheduling are applicable (see Section 2): this example cannot be synthesised using
conventional silicon compilers.
Soft Scheduling is expressive enough to deal with non-terminating resources:
a circuit is synthesised which contains an arbiter protecting the shared memory
whilst allowing proc1 and proc2 to operate in parallel.
Parallel Tasks Sharing Graphical Display
Consider a hardware design which can perform a number of tasks in parallel
with each task having the facility to update a graphical display. Many real-life
systems have this structure. For example in preparation for printing an ink-jet
printer performs a number of tasks in parallel: feed paper, reset position of print
head, check ink levels etc. Each one of these tasks can fail in which case an error
code is printed on the graphical display.
A controller for such a printer in SAFL may have the following structure:
extern display (data : 16) : unit
fun reset_head()
: unit = ...
if head_status <> 0 then
display(4) (* Error code 4 *)
else ...
fun feed_paper()
fun check_ink()
: unit = ... display(5) ...
: unit = ... display(6) ...
fun main()
: unit =
(feed_paper() || reset_head() || check_ink());
wait_for_next_job(); main()
Let us assume that each of the tasks terminates in a statically bounded time.
Given this assumption, both static scheduling and Soft Scheduling can be used
to ensure mutually exclusive access to display. It is interesting to compare and
contrast the circuits resulting from the application of these different techniques.
Since the tasks may invoke a common resource, applying static scheduling
techniques results in the tasks being serialised. In contrast, Soft Scheduling allows
the tasks to operate in parallel and automatically generates an arbiter which
dynamically schedules access to the shared display function.
Errors occur infrequently and hence contention for the display is rare. Under
this condition, and assuming that the tasks take all roughly the same amount
of time, Soft Scheduling yields a printer whose initialisation time is three times
faster than an equivalent statically scheduled printer. More generally, for a system with n balanced tasks, Soft Scheduling generates designs which are n times
Conclusions and Further Work
Soft Scheduling is a powerful technique which provides a number of advantages
over current scheduling technology:
More expressive: in contrast to existing scheduling methods, Soft Scheduling
can handle arbitrary networks of shared inter-dependent resources.
Increased efficiency: in some circumstances, controlling access to shared resources dynamically yields significantly better performance than statically
choosing a single schedule (see Example 5.3).
Higher level of abstraction: current hardware synthesis paradigms require a
designer to code arbiters explicitly at the structural level. Soft Scheduling
abstracts mutual exclusion concerns completely, increasing the readability
of source code without sacrificing efficiency.
One of the aims of the FLaSH Synthesis System is to facilitate the use of
source-level program transformation in order to investigate a range of possible designs arising from a single specification. We have shown that fold/unfold
transformations [3] can be applied to SAFL programs to explore various allocation/binding constraints [15]. In [16] we describe a SAFL transformation to
partition a design into hardware and software parts. The simplicity of our transformation system is partly due to the resource abstraction provided by Soft
Scheduling—transformations involving shared resources would be much more
complex if locking and arbitration details had to be considered at the SAFLlevel.
An arguable disadvantage of dynamic scheduling is that is makes the timing
behaviour of the final circuit difficult to analyse. Since access to shared resources
is resolved dynamically it becomes much harder to prove that real-time design
constraints are met. In future work we intend to investigate (a) the incorporation
of timing directives into the SAFL language; and (b) the static analysis of timing
properties for dynamically scheduled hardware systems.
When the parallel interleaving of non-terminating resources is required dynamic scheduling is essential (see Example 5.2); in other cases dynamic scheduling can offer increased performance (see Example 5.3). However, for fine-grained
sharing of smaller resources whose execution delays are known at compile-time
(such as arithmetic units), static scheduling techniques are more appropriate.
Soft Scheduling provides a powerful framework which strikes a compromise between the two approaches. The designer has the flexibility either:
– to describe a single static schedule (see Example 5.1) in which case dynamic
arbitration is optimised away; or
– to leave scheduling details to the compiler (see Example 5.3) in which case
dynamic arbitration is inserted where needed.
This research was supported by (UK) EPSRC grant GR/N64256: “A resourceaware functional language for hardware synthesis”.
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