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Embedded and IoT
edge-ai-camera
on-camera AI inference
smart doorbells
System
Techniques · 13
Primitives · 66
edge-ai-camera
Deterministic simulation testing
distributed · medium: Run the whole system in one thread under a seeded scheduler with simulated network, disk and clock, so every failure replays from its seed.
›
Primary-backup replication
distributed · medium: One node orders all writes and ships them to standbys; the acknowledgement rule (none, one, all) sets what a crash loses, and an external arbiter decides who takes over.
›
Assignment problem (Hungarian / Jon…
graphs · medium: Optimal one-to-one matching of rows to columns in O(n³) by growing a matching along shortest augmenting paths over dual potentials.
›
Multi-object tracking
machine-learning · medium: Turn per-frame detections (camera boxes or radar plots) into persistent IDs by predicting each track, gating, assigning and managing track births and deaths.
›
Object detection
machine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.
›
Coverage-guided fuzzing
security · medium: Mutate inputs, run an instrumented target, and keep any input that reaches a new code edge; feedback turns blind guessing into incremental search.
›
TLS 1.3
security · medium: The standard encrypted, authenticated transport; one round trip, only forward-secret AEAD suites, and little left to misconfigure.
›
Non-maximum suppression
machine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.
›
Kalman filter
robotics · low: Optimal recursive estimator for a linear system with Gaussian noise: fuses a motion model with noisy measurements into a state estimate plus its covariance.
›
Convolutional networks
ml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.
›
Hdr histogram
data-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentiles
SPSC ring buffer
data-structures · low: Bounded wait-free queue for exactly one producer and one consumer thread, built from an array and two indices with acquire/release ordering.
›
Low power duty cycling
embedded · low: MCU sleep states, wake sources and radio duty cycling to run years on a battery
Color space conversion
graphics · low: Convert pixels between transfer curves (sRGB, linear light), primaries (sRGB, Display P3, XYZ) and Y'CbCr, using tables for the curve and 3x3 matrices for the rest.
›
Image warping remap
graphics · low: Resampling with lookup maps: resize, lens undistortion, homographies, bilinear/bicubic
Process supervision
hardware-os · low: A parent process starts services, reaps their exits and restarts them under a delay and burst-limit policy, then stops them with SIGTERM before SIGKILL.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
›
Background subtraction
media · low: Running-average/MOG background model to detect motion in video frames
Video codecs
media · low: H.264/HEVC/AV1 basics: frames, GOPs, hardware decode/encode APIs and latency settings
Kernel fusion
ml-training · low: Merge chains of memory-bound GPU ops into one kernel, so intermediates stay in registers instead of round-tripping through HBM.
›
Tiled matrix multiplication
ml-training · low: Block GEMM into tiles held in shared memory and registers, so each byte loaded from DRAM feeds many FMAs; the core of fast GEMM.
›
Weight quantization
ml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.
›
+ 6 more techniques
+ 50 more primitives
System
Techniques · 13
Primitives · 66
edge-ai-camera
Deterministic simulation testing
distributed · medium: Run the whole system in one thread under a seeded scheduler with simulated network, disk and clock, so every failure replays from its seed.
›
Primary-backup replication
distributed · medium: One node orders all writes and ships them to standbys; the acknowledgement rule (none, one, all) sets what a crash loses, and an external arbiter decides who takes over.
›
Assignment problem (Hungarian / Jon…
graphs · medium: Optimal one-to-one matching of rows to columns in O(n³) by growing a matching along shortest augmenting paths over dual potentials.
›
Multi-object tracking
machine-learning · medium: Turn per-frame detections (camera boxes or radar plots) into persistent IDs by predicting each track, gating, assigning and managing track births and deaths.
›
Object detection
machine-learning · medium: A network turns an image into scored, classified boxes in one pass; anchor-based, anchor-free and set-prediction heads differ in how they assign labels and remove duplicates.
›
Coverage-guided fuzzing
security · medium: Mutate inputs, run an instrumented target, and keep any input that reaches a new code edge; feedback turns blind guessing into incremental search.
›
TLS 1.3
security · medium: The standard encrypted, authenticated transport; one round trip, only forward-secret AEAD suites, and little left to misconfigure.
›
Point-in-time recovery
storage · medium: Periodic base backups plus a continuously archived change log let you rebuild the state as of any instant, then branch from there.
›
Delta debugging
compilers · medium: Ddmin minimization of failing inputs or change sets
Fault injection
distributed · medium: Inject latency, errors, kills and partitions via proxies, netem or hooks
ARIES recovery
storage · medium: Three-pass crash recovery (analysis, redo, undo) for steal/no-force buffer pools; repeats history, then rolls back losers with compensation log records.
›
Syscall interception
security · medium: Intercept syscalls via ptrace, seccomp user notify or a user-space kernel
Buffer pool
storage · medium: The database's own page cache (frames, page table, pin counts, dirty bits, replacement) so it, not the OS, controls eviction, write order and I/O.
›
Non-maximum suppression
machine-learning · low: Keep the best-scoring candidate in each neighbourhood and drop the rest; greedy NMS, Soft-NMS and 1-D peak picking share one loop.
›
Kalman filter
robotics · low: Optimal recursive estimator for a linear system with Gaussian noise: fuses a motion model with noisy measurements into a state estimate plus its covariance.
›
Convolutional networks
ml-training · low: Layers that slide a small shared kernel over a grid, giving translation equivariance and few parameters; still the most compute-efficient choice for many vision tasks.
›
Hdr histogram
data-structures · low: Log-linear bucketed histograms with bounded relative error for latency percentiles
SPSC ring buffer
data-structures · low: Bounded wait-free queue for exactly one producer and one consumer thread, built from an array and two indices with acquire/release ordering.
›
Low power duty cycling
embedded · low: MCU sleep states, wake sources and radio duty cycling to run years on a battery
Color space conversion
graphics · low: Convert pixels between transfer curves (sRGB, linear light), primaries (sRGB, Display P3, XYZ) and Y'CbCr, using tables for the curve and 3x3 matrices for the rest.
›
Image warping remap
graphics · low: Resampling with lookup maps: resize, lens undistortion, homographies, bilinear/bicubic
Process supervision
hardware-os · low: A parent process starts services, reaps their exits and restarts them under a delay and burst-limit policy, then stops them with SIGTERM before SIGKILL.
›
SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
›
Embeddings
machine-learning · low: A learned table mapping discrete ids to dense vectors: gather forward, sparse scatter-add backward, row-wise optimizers to keep huge tables cheap.
›
Background subtraction
media · low: Running-average/MOG background model to detect motion in video frames
Video codecs
media · low: H.264/HEVC/AV1 basics: frames, GOPs, hardware decode/encode APIs and latency settings
Kernel fusion
ml-training · low: Merge chains of memory-bound GPU ops into one kernel, so intermediates stay in registers instead of round-tripping through HBM.
›
Tiled matrix multiplication
ml-training · low: Block GEMM into tiles held in shared memory and registers, so each byte loaded from DRAM feeds many FMAs; the core of fast GEMM.
›
Weight quantization
ml-training · low: Store weights as 8/4-bit integers with per-group scales; decode is memory-bound, so 4-bit weights give about 3-4x tokens/s and fit 4x larger models.
›
Mutual TLS
security · low: TLS where the client also proves a certificate key, so every connection carries a verified, rotatable machine identity.
›
Graceful draining
systems · low: On a termination signal, stop taking new work, finish in-flight work until a deadline, tell clients to reconnect elsewhere, then exit; clients resume from a token.
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Cooperative scheduling
concurrency · low: Green threads/coroutines yielding at await points on a user-space scheduler
Record replay
distributed · low: Record every nondeterministic input (time, randomness, I/O, model and tool outputs, per-tick player input) and re-execute deterministically for debugging, replays and state rebuild
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
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Leases
distributed · low: A lock that expires on its own: bounded-time ownership that survives crashes and partitions, given bounded clock drift and fencing tokens on writes.
›
Write-ahead log
storage · low: Append each change to a checksummed sequential log and fsync it before touching data pages; the basis of crash durability in nearly every database.
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Health checking
systems · low: Decide per backend whether to route to it using probes and real-traffic errors, with consecutive-count hysteresis, capped ejection and a panic fallback.
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d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
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Dijkstra's algorithm
graphs · low: Single-source shortest paths with non-negative edge weights by settling vertices in order of distance with a priority queue.
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Data augmentation
machine-learning · low: Generate label-preserving variants of each training example on the fly to enlarge the effective dataset and bake in invariances.
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Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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Code coverage instrumentation
compilers · low: Insert counters into compiled code to record which blocks, edges or lines ran; a spanning tree of the control-flow graph cuts the counters needed.
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Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
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Sanitizers
security · low: ASan/UBSan/TSan shadow memory and instrumentation to catch memory bugs
AEAD (AES-GCM, ChaCha20-Poly1305)
security · low: Authenticated encryption that encrypts and detects tampering in one call; the only kind of symmetric encryption you should use.
›
Cryptographic hashes (SHA-2, BLAKE3)
security · low: Collision-resistant fingerprints of data; SHA-256 for interoperability, BLAKE3 for speed, HMAC/HKDF for keyed uses.
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ECDH key exchange (X25519, P-256)
security · low: Each side multiplies the other's public curve point by its own scalar and both get the same point; always validate the peer key and run the result through a KDF.
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HKDF key derivation (extract-and-ex…
security · low: HMAC-based two-step key derivation: extract a uniform pseudorandom key from any secret, then expand it into as many context-bound keys as you need.
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X509 certificates
security · low: Certificate chains, path validation, SANs, revocation and short-lived issuance
Object storage
cloud · low: S3-compatible blob storage at cents per GB-month with 11 nines durability; pick by egress price, request price and latency, not by storage price alone.
›
Copy on write snapshots
storage · low: Point-in-time snapshots by sharing pages/extents and copying only on write
Failure bisection
search-sort · low: Binary search over commits, batches or inputs to isolate a failure
Transparent interception
networking · low: Redirect traffic to a local proxy (iptables TPROXY/REDIRECT, eBPF) without app changes
Ebpf
hardware-os · low: Verified in-kernel programs on hooks (kprobes, XDP, tc) with maps
Ptrace process control
hardware-os · low: Ptrace: attach, read and write registers and memory, single-step, intercept signals
Seccomp bpf
security · low: Syscall filtering with seccomp BPF programs
fsync, O_DSYNC and O_DIRECT
hardware-os · low: What fsync, fdatasync, O_DSYNC, O_DIRECT and F_FULLFSYNC really guarantee on Linux and macOS, and the exact call sequences for crash-safe files.
›
LRU and CLOCK
systems · low: LRU evicts the least recently used entry via hash map plus linked list; CLOCK approximates it with a reference bit, so hits take no lock.
›
S3-FIFO
systems · low: Three FIFO queues (small, main, ghost) that evict one-hit wonders quickly and keep reused objects; low miss ratios with no list moves on hits.
›
Control-flow graph
compilers · low: Split a linear instruction list into basic blocks and connect them by jumps; the graph every optimizer, SSA builder and dataflow pass walks.
›
False sharing
concurrency · low: Independent variables written by different cores on one cache line force the line to bounce between cores; pad or regroup hot data to fix it.
›
Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
›
Fencing tokens
distributed · low: A number that grows with every lock grant and is checked by the protected resource, so a paused ex-holder's late write is rejected.
›
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
›
Minimum spanning tree
graphs · low: Cheapest set of edges connecting all vertices; Kruskal sorts edges and joins components with union-find, Prim grows one tree, Boruvka merges in parallel rounds.
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Union-find
graphs · low: Disjoint-set forest with union by size and path compression; merges sets and answers same-set queries in effectively constant time.
›
CPU caches and the memory hierarchy
hardware-os · low: Sizes, line widths and latencies of L1/L2/L3/DRAM on modern x86 and Apple M-series, and the data-layout rules that follow from them.
›
CRC32C checksums
hardware-os · low: The Castagnoli CRC-32 with dedicated instructions on x86 (SSE4.2) and ARMv8. It is the standard fast integrity check for storage blocks, logs and network frames.
›
Gpu memory coalescing banking
hardware-os · low: Coalesced global loads, shared memory bank conflicts, L2 and HBM bandwidth on GPUs
CUDA and GPU languages
languages · low: Programming GPUs: thousands of lockstep threads, bound by memory bandwidth; libraries first, Triton for fused kernels, CUDA C++ for peak speed.
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Chroma subsampling
media · low: 4:2:0/4:2:2 colour plane downsampling and siting
AdamW
ml-training · low: Adam's per-parameter adaptive steps with weight decay applied directly to the weights; the default optimizer for transformers and LLM pretraining.
›
Cuda graphs
ml-training · low: Capturing a sequence of GPU kernel launches once and replaying it to remove CPU launch overhead
Reverse-mode automatic differentiat…
ml-training · low: Record the forward computation on a tape, then sweep it backwards once to get the gradient of a scalar loss for all inputs.
›
Low precision number formats
numerics · low: Bf16, fp16, fp8 (E4M3, E5M2), MX block formats and int4: ranges, rounding, scaling
LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
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Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
›
Constant time code
security · low: Writing secret-dependent code without branches or memory lookups on secrets; constant-time compare and select
Csprng
security · low: OS-backed cryptographically secure random numbers (getrandom), and when a fast PRNG is not enough
Copy for AI
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