Phase 3A technical baseline POPULATED
Engineering overview
Digital signal processors are optimized for sustained numerical work on streaming data. Architectural features commonly include multiply-accumulate capability, pipelined/vector execution, specialized addressing, parallel memory access and deterministic data movement that support filters, transforms, communications, control and sensor processing.
DSP design emphasizes sample deadlines and dataflow. The question is not only how many operations a device can execute, but whether input, computation and output can be scheduled continuously without overruns or unpredictable latency.
Core concepts
MAC throughputMultiply-accumulate is a core primitive for FIR filters, dot products and matrix/vector operations.
Vector/VLIW executionMultiple operations can be issued per cycle when software exposes sufficient parallelism.
Streaming memoryCircular buffers, DMA and multiple data paths reduce overhead for continuous sample streams.
Numeric formatFixed, floating and mixed precision trade dynamic range, determinism, area, power and performance.
Engineering workflow
- Convert algorithm requirements into sample rates, frame sizes, operations per sample and latency budgets.
- Choose fixed/floating precision using quantified error and dynamic-range analysis.
- Lay out memory/DMA flow before hand-optimizing compute kernels.
- Pipeline input, processing and output so data movement overlaps computation.
- Measure worst-case real-time execution, not just average benchmark throughput.
Tradeoffs & failure modes
- Optimizing arithmetic while starving the core for data.
- Using unnecessary floating-point precision in power-constrained systems.
- Ignoring DMA contention and memory-bank conflicts.
- Meeting average throughput but missing frame deadlines.